

'''simple docstring'''






import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowerCAmelCase__											=	{
    '''/attention/''': '''/0/SelfAttention/''',
    '''/self_attention/''': '''/0/SelfAttention/''',
    '''/encoder_decoder_attention/''': '''/1/EncDecAttention/''',
    '''value''': '''v''',
    '''query''': '''q''',
    '''key''': '''k''',
    '''out''': '''o''',
    '''pre_self_attention_layer_norm''': '''0/layer_norm''',
    '''pre_cross_attention_layer_norm''': '''1/layer_norm''',
    '''pre_attention_layer_norm''': '''0/layer_norm''',  # previously 1, but seems wrong
    '''token_embedder''': '''shared''',
    '''encoder_norm''': '''final_layer_norm''',
    '''decoder_norm''': '''final_layer_norm''',
    '''relpos_bias/rel_embedding''': '''block/0/layer/0/SelfAttention/relative_attention_bias/weight''',
    '''router/router_weights/w/''': '''router/classifier/''',
    '''roer/roer_weights/w/''': '''router/classifier/''',
    '''logits_dense''': '''lm_head''',
}


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = list(s_dict.keys()      )
 for key in keys:
  __lowercase							  = R'''.*/layers_(\d+)'''
  __lowercase							  = key
  if re.match(A__     ,				A__      ):
   __lowercase							  = re.sub(R'''layers_(\d+)'''     ,				R'''block/\1/layer'''     ,				A__      )

  __lowercase							  = R'''(encoder|decoder)\/'''

  if re.match(A__     ,				A__      ):
   __lowercase							  = re.match(A__     ,				A__      ).groups()
   if groups[0] == "encoder":
    __lowercase							  = re.sub(R'''/mlp/'''     ,				R'''/1/mlp/'''     ,				A__      )
    __lowercase							  = re.sub(R'''/pre_mlp_layer_norm/'''     ,				R'''/1/layer_norm/'''     ,				A__      )

   elif groups[0] == "decoder":
    __lowercase							  = re.sub(R'''/mlp/'''     ,				R'''/2/mlp/'''     ,				A__      )
    __lowercase							  = re.sub(R'''/pre_mlp_layer_norm/'''     ,				R'''/2/layer_norm/'''     ,				A__      )

        # 2. Convert other classic mappings
  for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
   if old_key in new_key:
    __lowercase							  = new_key.replace(A__     ,				A__      )

  print(F"{key} -> {new_key}"      )
  __lowercase							  = s_dict.pop(A__      )

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowercase							  = s_dict[
      '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
  ].T
 if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  __lowercase							  = s_dict[
      '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
  ].T

 # 3. Take extra care of the EXPERTS layer
 for key in list(s_dict.keys()      ):
  if "expert" in key:
   __lowercase							  = s_dict[key].shape[0]
   __lowercase							  = s_dict[key]
   for idx in range(A__      ):
    __lowercase							  = expert_weihts[idx]
    print(F"{key} -> {key.replace('expert/'     ,				'nested fstring'      )}"      )

   s_dict.pop(A__      )

 return s_dict


lowerCAmelCase__											=	{
    '''NUM_ENCODER_LAYERS''': '''num_layers''',
    '''NUM_DECODER_LAYERS''': '''num_decoder_layers''',
    '''NUM_HEADS''': '''num_heads''',
    '''HEAD_DIM''': '''d_kv''',
    '''EMBED_DIM''': '''d_model''',
    '''MLP_DIM''': '''d_ff''',
    '''NUM_SELECTED_EXPERTS''': '''num_selected_experts''',
    '''NUM_ENCODER_SPARSE_LAYERS''': '''num_sparse_encoder_layers''',
    '''NUM_DECODER_SPARSE_LAYERS''': '''num_sparse_decoder_layers''',
    '''dense.MlpBlock.activations''': '''feed_forward_proj''',
}


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 import regex as re

 with open(A__     ,				'''r'''      ) as f:
  __lowercase							  = f.read()

 __lowercase							  = re.findall(R'''(.*) = ([0-9.]*)'''     ,				A__      )
 __lowercase							  = {}
 for param, value in regex_match:
  if param in GIN_TO_CONFIG_MAPPING and value != "":
   __lowercase							  = float(A__      ) if '''.''' in value else int(A__      )

 __lowercase							  = re.findall(R'''(.*activations) = \(\'(.*)\',\)'''     ,				A__      )[0]
 __lowercase							  = str(activation[1]      )

 __lowercase							  = num_experts
 __lowercase							  = SwitchTransformersConfig(**A__      )
 return config


def 		_A     (    A__     ,				A__     ,				A__=None     ,				A__="./"     ,				A__=8      ):






 """simple docstring"""



 print(F"Loading flax weights from : {flax_checkpoint_path}"      )
 __lowercase							  = checkpoints.load_tax_checkpoint(A__      )

 if gin_file is not None:
  __lowercase							  = convert_gin_to_config(A__     ,				A__      )
 else:
  __lowercase							  = SwitchTransformersConfig.from_pretrained(A__      )

 __lowercase							  = SwitchTransformersForConditionalGeneration(A__      )

 __lowercase							  = flax_params['''target''']
 __lowercase							  = flatten_dict(A__     ,				sep='''/'''      )
 __lowercase							  = rename_keys(A__      )
 __lowercase							  = unflatten_dict(A__     ,				sep='''/'''      )

 # Load the flax params in the PT model
 load_flax_weights_in_pytorch_model(A__     ,				A__      )

 print(F"Save PyTorch model to {pytorch_dump_path}"      )
 pt_model.save_pretrained(A__      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--switch_t5x_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'''
         ''' model architecture. If not provided, a `gin_file` has to be provided.'''
     ),
 )
 parser.add_argument(
     '''--gin_file''',
     default=None,
     type=str,
     required=False,
     help='''Path to the gin config file. If not provided, a `config_file` has to be passed   ''',
 )
 parser.add_argument(
     '''--config_name''', default=None, type=str, required=False, help='''Config name of SwitchTransformers model.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output pytorch model.'''
 )
 parser.add_argument('''--num_experts''', default=8, type=int, required=False, help='''Number of experts''')
 lowerCAmelCase__											=	parser.parse_args()
 convert_flax_checkpoint_to_pytorch(
     args.switch_tax_checkpoint_path,
     args.config_name,
     args.gin_file,
     args.pytorch_dump_folder_path,
     args.num_experts,
 )




'''simple docstring'''






import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase__											=	version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__     ,				A__=False     ,				):






 """simple docstring"""



 output_path.parent.mkdir(parents=A__     ,				exist_ok=A__      )
 # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
 # so we check the torch version for backwards compatibility
 if is_torch_less_than_1_11:
  export(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				use_external_data_format=A__     ,				enable_onnx_checker=A__     ,				opset_version=A__     ,				)
 else:
  export(
      A__     ,				A__     ,				f=output_path.as_posix()     ,				input_names=A__     ,				output_names=A__     ,				dynamic_axes=A__     ,				do_constant_folding=A__     ,				opset_version=A__     ,				)


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__     ,				A__ = False      ):






 """simple docstring"""



 __lowercase							  = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  __lowercase							  = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''      )
 else:
  __lowercase							  = '''cpu'''
 __lowercase							  = Path(A__      )

 # VAE DECODER
 __lowercase							  = AutoencoderKL.from_pretrained(model_path + '''/vae'''      )
 __lowercase							  = vae_decoder.config.latent_channels
 # forward only through the decoder part
 __lowercase							  = vae_decoder.decode
 onnx_export(
     A__     ,				model_args=(
         torch.randn(1     ,				A__     ,				25     ,				25      ).to(device=A__     ,				dtype=A__      ),
         False,
     )     ,				output_path=output_path / '''vae_decoder''' / '''model.onnx'''     ,				ordered_input_names=['''latent_sample''', '''return_dict''']     ,				output_names=['''sample''']     ,				dynamic_axes={
         '''latent_sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
     }     ,				opset=A__     ,				)
 del vae_decoder


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument(
     '''--model_path''',
     type=str,
     required=True,
     help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
 )

 parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
 parser.add_argument(
     '''--opset''',
     default=14,
     type=int,
     help='''The version of the ONNX operator set to use.''',
 )
 parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

 lowerCAmelCase__											=	parser.parse_args()
 print(args.output_path)
 convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
 print('''SD: Done: ONNX''')

