





'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a   	=    logging.get_logger(__name__)

a   	=    {
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						'wavlm'





   def __init__(       self					:      List[Any]					,lowerCamelCase					:      List[str]=32					,lowerCamelCase					:      Any=768					,lowerCamelCase					:      Optional[int]=12					,lowerCamelCase					:      Union[str, Any]=12					,lowerCamelCase					:      List[str]=3072					,lowerCamelCase					:      List[str]="gelu"					,lowerCamelCase					:      Union[str, Any]=0.1					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      Union[str, Any]=0.1					,lowerCamelCase					:      Dict=0.0					,lowerCamelCase					:      int=0.1					,lowerCamelCase					:      Union[str, Any]=0.1					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      Optional[int]=1E-5					,lowerCamelCase					:      int="group"					,lowerCamelCase					:      Tuple="gelu"					,lowerCamelCase					:      Optional[Any]=(512, 512, 512, 512, 512, 512, 512)					,lowerCamelCase					:      Optional[Any]=(5, 2, 2, 2, 2, 2, 2)					,lowerCamelCase					:      List[Any]=(10, 3, 3, 3, 3, 2, 2)					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      Tuple=128					,lowerCamelCase					:      Union[str, Any]=16					,lowerCamelCase					:      int=320					,lowerCamelCase					:      Dict=800					,lowerCamelCase					:      int=False					,lowerCamelCase					:      Optional[Any]=True					,lowerCamelCase					:      List[Any]=0.05					,lowerCamelCase					:      Dict=10					,lowerCamelCase					:      Optional[int]=2					,lowerCamelCase					:      List[str]=0.0					,lowerCamelCase					:      Optional[int]=10					,lowerCamelCase					:      str=320					,lowerCamelCase					:      Dict=2					,lowerCamelCase					:      Tuple=0.1					,lowerCamelCase					:      Dict=100					,lowerCamelCase					:      Optional[Any]=256					,lowerCamelCase					:      Optional[int]=256					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      List[str]="mean"					,lowerCamelCase					:      Dict=False					,lowerCamelCase					:      int=False					,lowerCamelCase					:      Tuple=256					,lowerCamelCase					:      str=(512, 512, 512, 512, 1500)					,lowerCamelCase					:      List[Any]=(5, 3, 3, 1, 1)					,lowerCamelCase					:      List[Any]=(1, 2, 3, 1, 1)					,lowerCamelCase					:      str=512					,lowerCamelCase					:      Optional[Any]=80					,lowerCamelCase					:      Optional[int]=0					,lowerCamelCase					:      Dict=1					,lowerCamelCase					:      Any=2					,lowerCamelCase					:      List[str]=False					,lowerCamelCase					:      Optional[Any]=3					,lowerCamelCase					:      int=2					,lowerCamelCase					:      int=3					,lowerCamelCase					:      List[Any]=None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase					,pad_token_id=lowerCamelCase					,bos_token_id=lowerCamelCase					,eos_token_id=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							hidden_size
      __SCREAMING_SNAKE_CASE   				=							feat_extract_norm
      __SCREAMING_SNAKE_CASE   				=							feat_extract_activation
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							conv_bias
      __SCREAMING_SNAKE_CASE   				=							num_buckets
      __SCREAMING_SNAKE_CASE   				=							max_bucket_distance
      __SCREAMING_SNAKE_CASE   				=							num_conv_pos_embeddings
      __SCREAMING_SNAKE_CASE   				=							num_conv_pos_embedding_groups
      __SCREAMING_SNAKE_CASE   				=							len(self.conv_dim     )
      __SCREAMING_SNAKE_CASE   				=							num_hidden_layers
      __SCREAMING_SNAKE_CASE   				=							intermediate_size
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout
      __SCREAMING_SNAKE_CASE   				=							attention_dropout
      __SCREAMING_SNAKE_CASE   				=							activation_dropout
      __SCREAMING_SNAKE_CASE   				=							feat_proj_dropout
      __SCREAMING_SNAKE_CASE   				=							final_dropout
      __SCREAMING_SNAKE_CASE   				=							layerdrop
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							num_ctc_classes
      __SCREAMING_SNAKE_CASE   				=							vocab_size
      __SCREAMING_SNAKE_CASE   				=							do_stable_layer_norm
      __SCREAMING_SNAKE_CASE   				=							use_weighted_layer_sum
      __SCREAMING_SNAKE_CASE   				=							classifier_proj_size

      if (
          (len(self.conv_stride     ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
          or (len(self.conv_dim     ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
             """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
             f""" {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,"""
             f""" `len(config.conv_kernel) = {len(self.conv_kernel     )}`."""     )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __SCREAMING_SNAKE_CASE   				=							apply_spec_augment
      __SCREAMING_SNAKE_CASE   				=							mask_time_prob
      __SCREAMING_SNAKE_CASE   				=							mask_time_length
      __SCREAMING_SNAKE_CASE   				=							mask_time_min_masks
      __SCREAMING_SNAKE_CASE   				=							mask_feature_prob
      __SCREAMING_SNAKE_CASE   				=							mask_feature_length

      # parameters for pretraining with codevector quantized representations
      __SCREAMING_SNAKE_CASE   				=							num_codevectors_per_group
      __SCREAMING_SNAKE_CASE   				=							num_codevector_groups
      __SCREAMING_SNAKE_CASE   				=							contrastive_logits_temperature
      __SCREAMING_SNAKE_CASE   				=							num_negatives
      __SCREAMING_SNAKE_CASE   				=							codevector_dim
      __SCREAMING_SNAKE_CASE   				=							proj_codevector_dim
      __SCREAMING_SNAKE_CASE   				=							diversity_loss_weight

      # ctc loss
      __SCREAMING_SNAKE_CASE   				=							ctc_loss_reduction
      __SCREAMING_SNAKE_CASE   				=							ctc_zero_infinity

      # adapter
      __SCREAMING_SNAKE_CASE   				=							add_adapter
      __SCREAMING_SNAKE_CASE   				=							adapter_kernel_size
      __SCREAMING_SNAKE_CASE   				=							adapter_stride
      __SCREAMING_SNAKE_CASE   				=							num_adapter_layers
      __SCREAMING_SNAKE_CASE   				=							output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      __SCREAMING_SNAKE_CASE   				=							classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							list(lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							xvector_output_dim





   @property
   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      return functools.reduce(operator.mul					,self.conv_stride					,1     )












'''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   	=    {
    "/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     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							R"""(encoder|decoder)\/"""

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

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

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

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

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

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

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

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,						__UpperCAmelCase   )[0]
   __SCREAMING_SNAKE_CASE   				=							str(activation[1]   )

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
   __SCREAMING_SNAKE_CASE   				=							checkpoints.load_tax_checkpoint(__UpperCAmelCase   )

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							unflatten_dict(__UpperCAmelCase				,						sep="""/"""   )

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

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


if __name__ == "__main__":
    a   	=    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   	=    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,
    )





