






'''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
a_						:       Optional[Any]      			=				{
    """/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 __snake_case      (   UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						list(s_dict.keys()	)
 for key in keys:
  lowerCamelCase_  				=						r'.*/layers_(\d+)'
  lowerCamelCase_  				=						key
  if re.match(__lowercase      ,       __lowercase	):
   lowerCamelCase_  				=						re.sub(r"layers_(\d+)"      ,       r"block/\1/layer"      ,       __lowercase	)

  lowerCamelCase_  				=						r'(encoder|decoder)\/'

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

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

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

  print(F'''{key} -> {new_key}'''	)
  lowerCamelCase_  				=						s_dict.pop(__lowercase	)

 if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
  lowerCamelCase_  				=						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:
  lowerCamelCase_  				=						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:
   lowerCamelCase_  				=						s_dict[key].shape[0]
   lowerCamelCase_  				=						s_dict[key]
   for idx in range(__lowercase	):
    lowerCamelCase_  				=						expert_weihts[idx]
    print(F'''{key} -> {key.replace("expert/"      ,       "nested fstring"	)}'''	)

   s_dict.pop(__lowercase	)

 return s_dict


a_						:       Any      			=				{
    """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 __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Union[str, Any]	):
 import regex as re

 with open(__lowercase      ,       "r"	) as f:
  lowerCamelCase_  				=						f.read()

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

 lowerCamelCase_  				=						re.findall(r"(.*activations) = \(\'(.*)\',\)"      ,       __lowercase	)[0]
 lowerCamelCase_  				=						str(activation[1]	)

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





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		Tuple=None      ,       UpperCAmelCase_		:		Tuple="./"      ,       UpperCAmelCase_		:		Tuple=8	):
 print(F'''Loading flax weights from : {flax_checkpoint_path}'''	)
 lowerCamelCase_  				=						checkpoints.load_tax_checkpoint(__lowercase	)

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

 lowerCamelCase_  				=						SwitchTransformersForConditionalGeneration(__lowercase	)

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

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

 print(F'''Save PyTorch model to {pytorch_dump_path}'''	)
 pt_model.save_pretrained(__lowercase	)


if __name__ == "__main__":
     a_						:       Tuple      			=				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""")
     a_						:       str      			=				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'''

def __snake_case      (   UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
 return "\n".join(
     F'''{number} * {i} = {number * i}''' for i in range(1      ,       number_of_terms + 1	)	)


if __name__ == "__main__":
     print(multiplication_table(number=5, number_of_terms=10))



