





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
lowercase      :       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 A_	(		A__				)  ->		Any:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       a__						:	Dict =				list(s_dict.keys()				)
       for key in keys:
              a__						:	Dict =				R'.*/layers_(\d+)'
              a__						:	Tuple =				key
              if re.match(A__		,      A__				):
                     a__						:	Dict =				re.sub(R'layers_(\d+)'		,      R'block/\1/layer'		,      A__				)

              a__						:	Tuple =				R'(encoder|decoder)\/'

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

                     elif groups[0] == "decoder":
                            a__						:	Any =				re.sub(R'/mlp/'		,      R'/2/mlp/'		,      A__				)
                            a__						:	str =				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:
                            a__						:	Union[str, Any] =				new_key.replace(A__		,      A__				)

              print(F'{key} -> {new_key}'				)
              a__						:	str =				s_dict.pop(A__				)

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              a__						:	Optional[int] =				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:
              a__						:	List[Any] =				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:
                     a__						:	Tuple =				s_dict[key].shape[0]
                     a__						:	Optional[Any] =				s_dict[key]
                     for idx in range(A__				):
                            a__						:	List[str] =				expert_weihts[idx]
                            print(F'{key} -> {key.replace("expert/"		,      "nested fstring"				)}'				)

                     s_dict.pop(A__				)

       return s_dict


lowercase      :       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 A_	(		A__		,      A__				)  ->		int:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(A__		,      'r'				) as f:
              a__						:	Union[str, Any] =				f.read()

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

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

       a__						:	List[str] =				num_experts
       a__						:	Optional[Any] =				SwitchTransformersConfig(**A__				)
       return config



def A_	(		A__		,      A__		,      A__=None		,      A__="./"		,      A__=8				)  ->		Tuple:
       # Initialise PyTorch model

       print(F'Loading flax weights from : {flax_checkpoint_path}'				)
       a__						:	Union[str, Any] =				checkpoints.load_tax_checkpoint(A__				)

       if gin_file is not None:
              a__						:	Union[str, Any] =				convert_gin_to_config(A__		,      A__				)
       else:
              a__						:	Union[str, Any] =				SwitchTransformersConfig.from_pretrained(A__				)

       a__						:	int =				SwitchTransformersForConditionalGeneration(A__				)

       a__						:	Any =				flax_params['target']
       a__						:	Any =				flatten_dict(A__		,      sep='/'				)
       a__						:	List[str] =				rename_keys(A__				)
       a__						:	Optional[Any] =				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__":
    lowercase      :       int									=  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""")
    lowercase      :       Optional[Any]									=  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 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  stooge(snake_case__		,   0		,   len(snake_case__    ) - 1    )
  return arr


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any		,   snake_case__							:					int    ):
  """simple docstring"""

  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _snake_case		,					_snake_case							:		Tuple								=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _snake_case							:		Dict								=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )

    # Recursively sort last 2/3 elements
    stooge(snake_case__		,   i + t		,   (snake_case__)    )

    # Recursively sort first 2/3 elements
    stooge(snake_case__		,   snake_case__		,   (h - t)    )


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(stooge_sort(unsorted))


