
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__         ={
    '/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   __UpperCamelCase	(							lowerCAmelCase__				:							Dict    ):
 __a					:    str													=    list(s_dict.keys()    )
 for key in keys:
  __a					:    Union[str, Any]													=    R".*/layers_(\d+)"
  __a					:    Optional[int]													=    key
  if re.match(lowerCAmelCase__    ,  lowerCAmelCase__    ):
   __a					:    Tuple													=    re.sub(R'''layers_(\d+)'''    ,  R'''block/\1/layer'''    ,  lowerCAmelCase__    )

  __a					:    Dict													=    R"(encoder|decoder)\/"

  if re.match(lowerCAmelCase__    ,  lowerCAmelCase__    ):
   __a					:    Dict													=    re.match(lowerCAmelCase__    ,  lowerCAmelCase__    ).groups()
   if groups[0] == "encoder":
    __a					:    Optional[Any]													=    re.sub(R'''/mlp/'''    ,  R'''/1/mlp/'''    ,  lowerCAmelCase__    )
    __a					:    Optional[int]													=    re.sub(R'''/pre_mlp_layer_norm/'''    ,  R'''/1/layer_norm/'''    ,  lowerCAmelCase__    )

   elif groups[0] == "decoder":
    __a					:    Any													=    re.sub(R'''/mlp/'''    ,  R'''/2/mlp/'''    ,  lowerCAmelCase__    )
    __a					:    str													=    re.sub(R'''/pre_mlp_layer_norm/'''    ,  R'''/2/layer_norm/'''    ,  lowerCAmelCase__    )

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

  print(f"{key} -> {new_key}"    )
  __a					:    int													=    s_dict.pop(lowerCAmelCase__    )

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

   s_dict.pop(lowerCAmelCase__    )

 return s_dict


lowercase__         ={
    '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   __UpperCamelCase	(							lowerCAmelCase__				:							List[str]    ,  lowerCAmelCase__				:							Any    ):
 import regex as re

 with open(lowerCAmelCase__    ,  '''r'''    ) as f:
  __a					:    Dict													=    f.read()

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

 __a					:    Any													=    re.findall(R'''(.*activations) = \(\'(.*)\',\)'''    ,  lowerCAmelCase__    )[0]
 __a					:    Any													=    str(activation[1]    )

 __a					:    Optional[int]													=    num_experts
 __a					:    Any													=    SwitchTransformersConfig(**lowerCAmelCase__    )
 return config




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ,  lowerCAmelCase__				:							Optional[int]    ,  lowerCAmelCase__				:							List[str]=None    ,  lowerCAmelCase__				:							Tuple="./"    ,  lowerCAmelCase__				:							Optional[int]=8    ):
 print(f"Loading flax weights from : {flax_checkpoint_path}"    )
 __a					:    Optional[Any]													=    checkpoints.load_tax_checkpoint(lowerCAmelCase__    )

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

 __a					:    Optional[int]													=    SwitchTransformersForConditionalGeneration(lowerCAmelCase__    )

 __a					:    Optional[Any]													=    flax_params["target"]
 __a					:    int													=    flatten_dict(lowerCAmelCase__    ,  sep='''/'''    )
 __a					:    Optional[int]													=    rename_keys(lowerCAmelCase__    )
 __a					:    str													=    unflatten_dict(lowerCAmelCase__    ,  sep='''/'''    )

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

 print(f"Save PyTorch model to {pytorch_dump_path}"    )
 pt_model.save_pretrained(lowerCAmelCase__    )


if __name__ == "__main__":
      lowercase__         =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__         =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 warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





