



'''simple docstring'''
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand






def A__       (	UpperCAmelCase_				):
						return EnvironmentCommand()






class 				lowercase__      (				lowercase  ):





	@staticmethod
	def       UpperCamelCase_    (					lowerCamelCase__   :							ArgumentParser					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  parser.add_parser('env'					)
							download_parser.set_defaults(func=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  huggingface_hub.__version__

							_UpperCamelCase   :						Tuple												=  'not installed'
							_UpperCamelCase   :						str												=  'NA'
							if is_torch_available():
													import torch

													_UpperCamelCase   :						Dict												=  torch.__version__
													_UpperCamelCase   :						List[Any]												=  torch.cuda.is_available()

							_UpperCamelCase   :						Any												=  'not installed'
							if is_transformers_available():
													import transformers

													_UpperCamelCase   :						Tuple												=  transformers.__version__

							_UpperCamelCase   :						List[Any]												=  'not installed'
							if is_accelerate_available():
													import accelerate

													_UpperCamelCase   :						Union[str, Any]												=  accelerate.__version__

							_UpperCamelCase   :						List[Any]												=  'not installed'
							if is_xformers_available():
													import xformers

													_UpperCamelCase   :						Any												=  xformers.__version__

							_UpperCamelCase   :						Optional[Any]												=  {
							    '`diffusers` version': version,
							    'Platform': platform.platform(),
							    'Python version': platform.python_version(),
							    'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
							    'Huggingface_hub version': hub_version,
							    'Transformers version': transformers_version,
							    'Accelerate version': accelerate_version,
							    'xFormers version': xformers_version,
							    'Using GPU in script?': '<fill in>',
							    'Using distributed or parallel set-up in script?': '<fill in>',
							}

							print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'					)
							print(self.format_dict(lowerCamelCase__					)					)

							return info





	@staticmethod
	def       UpperCamelCase_    (					lowerCamelCase__   :							Any					):







							'''simple docstring'''
							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]					) + "\n"







'''simple docstring'''
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def A__       (	UpperCAmelCase_				):
						if "model" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('model.'    ,    ''				)
						if "norm1" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('norm1'    ,    'attention.output.LayerNorm'				)
						if "norm2" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('norm2'    ,    'output.LayerNorm'				)
						if "norm" in orig_key:
												_UpperCamelCase   :						str												=  orig_key.replace('norm'    ,    'LayerNorm'				)
						if "transformer" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.split('.'				)[0].split('_'				)[-1]
												_UpperCamelCase   :						Tuple												=  orig_key.replace(f'transformer_{layer_num}'    ,    f'encoder.layer.{layer_num}'				)
						if "mha.attn" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('mha.attn'    ,    'attention.self'				)
						if "mha" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('mha'    ,    'attention'				)
						if "W_q" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('W_q'    ,    'self.query'				)
						if "W_k" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_k'    ,    'self.key'				)
						if "W_v" in orig_key:
												_UpperCamelCase   :						List[Any]												=  orig_key.replace('W_v'    ,    'self.value'				)
						if "ff1" in orig_key:
												_UpperCamelCase   :						Optional[Any]												=  orig_key.replace('ff1'    ,    'intermediate.dense'				)
						if "ff2" in orig_key:
												_UpperCamelCase   :						List[str]												=  orig_key.replace('ff2'    ,    'output.dense'				)
						if "ff" in orig_key:
												_UpperCamelCase   :						Union[str, Any]												=  orig_key.replace('ff'    ,    'output.dense'				)
						if "mlm_class" in orig_key:
												_UpperCamelCase   :						Tuple												=  orig_key.replace('mlm.mlm_class'    ,    'cls.predictions.decoder'				)
						if "mlm" in orig_key:
												_UpperCamelCase   :						int												=  orig_key.replace('mlm'    ,    'cls.predictions.transform'				)
						if "cls" not in orig_key:
												_UpperCamelCase   :						Optional[int]												=  'yoso.' + orig_key

						return orig_key






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase   :						Any												=  orig_state_dict.pop(UpperCAmelCase_				)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		_UpperCamelCase   :						Union[str, Any]												=  val

						_UpperCamelCase   :						Any												=  orig_state_dict['cls.predictions.decoder.bias']
						_UpperCamelCase   :						Union[str, Any]												=  torch.arange(UpperCAmelCase_				).expand((1, -1)				) + 2

						return orig_state_dict






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						List[Any]												=  torch.load(UpperCAmelCase_    ,    map_location='cpu'				)['model_state_dict']
						_UpperCamelCase   :						List[str]												=  YosoConfig.from_json_file(UpperCAmelCase_				)
						_UpperCamelCase   :						Union[str, Any]												=  YosoForMaskedLM(UpperCAmelCase_				)

						_UpperCamelCase   :						Tuple												=  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__":
					snake_case_					:      int    			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					snake_case_					:      List[Any]    			=    parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)


