



'''simple docstring'''





import mpmath  # for roots of unity
import numpy as np






class __A     :



 '''simple docstring'''







 def __init__(self					,  A=None					,  A=None			)       ->	Any:


      """simple docstring"""



      _a           =    list(poly_a or [0]			)[:]
      _a           =    list(poly_b or [0]			)[:]

      # Remove leading zero coefficients
      while self.polyA[-1] == 0:
           self.polyA.pop()
      _a           =    len(self.polyA			)

      while self.polyB[-1] == 0:
           self.polyB.pop()
      _a           =    len(self.polyB			)

      # Add 0 to make lengths equal a power of 2
      _a           =    int(
          2 ** np.ceil(np.loga(len(self.polyA			) + len(self.polyB			) - 1			)			)			)

      while len(self.polyA			) < self.c_max_length:
           self.polyA.append(0			)
      while len(self.polyB			) < self.c_max_length:
           self.polyB.append(0			)
      # A complex root used for the fourier transform
      _a           =    complex(mpmath.root(x=1					,  n=self.c_max_length					,  k=1			)			)

      # The product
      _a           =    self.__multiply()



 def    a__    (self					,  A			)       ->	Tuple:


      """simple docstring"""



      _a           =    [[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
      # Corner case
      if len(A			) <= 1:
           return dft[0]
      #
      _a           =    self.c_max_length // 2
      while next_ncol > 0:
           _a           =    [[] for i in range(A			)]
           _a           =    self.root**next_ncol

           # First half of next step
           _a           =    1
           for j in range(self.c_max_length // (next_ncol * 2)			):
                for i in range(A			):
                     new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]			)
                current_root *= root
           # Second half of next step
           _a           =    1
           for j in range(self.c_max_length // (next_ncol * 2)			):
                for i in range(A			):
                     new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]			)
                current_root *= root
           # Update
           _a           =    new_dft
           _a           =    next_ncol // 2
      return dft[0]



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    self.__dft('''A'''			)
      _a           =    self.__dft('''B'''			)
      _a           =    [[dft_a[i] * dft_b[i] for i in range(self.c_max_length			)]]
      del dft_a
      del dft_b

      # Corner Case
      if len(inverce_c[0]			) <= 1:
           return inverce_c[0]
      # Inverse DFT
      _a           =    2
      while next_ncol <= self.c_max_length:
           _a           =    [[] for i in range(A			)]
           _a           =    self.root ** (next_ncol // 2)
           _a           =    1
           # First half of next step
           for j in range(self.c_max_length // next_ncol			):
                for i in range(next_ncol // 2			):
                     # Even positions
                     new_inverse_c[i].append(
                         (
                             inverce_c[i][j]
                             + inverce_c[i][j + self.c_max_length // next_ncol]
                         )
                         / 2			)
                     # Odd positions
                     new_inverse_c[i + next_ncol // 2].append(
                         (
                             inverce_c[i][j]
                             - inverce_c[i][j + self.c_max_length // next_ncol]
                         )
                         / (2 * current_root)			)
                current_root *= root
           # Update
           _a           =    new_inverse_c
           next_ncol *= 2
      # Unpack
      _a           =    [round(x[0].real					,  8			) + round(x[0].imag					,  8			) * 1J for x in inverce_c]

      # Remove leading 0's
      while inverce_c[-1] == 0:
           inverce_c.pop()
      return inverce_c



 def __str__(self			)       ->	Union[str, Any]:


      """simple docstring"""



      _a           =    '''A = ''' + ''' + '''.join(
          f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]			)			)
      _a           =    '''B = ''' + ''' + '''.join(
          f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]			)			)
      _a           =    '''A*B = ''' + ''' + '''.join(
          f'''{coef}*x^{i}''' for coef, i in enumerate(self.product			)			)

      return f'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()








'''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
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 lowerCAmelCase    (__A):



     """simple docstring"""



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

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

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

               elif groups[0] == "decoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/2/mlp/'''    ,      __A)
                    _a           =    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           =    new_key.replace(__A    ,      __A)

          print(F'''{key} -> {new_key}''')
          _a           =    s_dict.pop(__A)

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

               s_dict.pop(__A)

     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 lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     import regex as re

     with open(__A    ,      '''r''') as f:
          _a           =    f.read()

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

     _a           =    re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    ,      __A)[0]
     _a           =    str(activation[1])

     _a           =    num_experts
     _a           =    SwitchTransformersConfig(**__A)
     return config




def lowerCAmelCase    (__A    ,      __A    ,      __A=None    ,      __A="./"    ,      __A=8):



     """simple docstring"""



     print(F'''Loading flax weights from : {flax_checkpoint_path}''')
     _a           =    checkpoints.load_tax_checkpoint(__A)

     if gin_file is not None:
          _a           =    convert_gin_to_config(__A    ,      __A)
     else:
          _a           =    SwitchTransformersConfig.from_pretrained(__A)

     _a           =    SwitchTransformersForConditionalGeneration(__A)

     _a           =    flax_params['''target''']
     _a           =    flatten_dict(__A    ,      sep='''/''')
     _a           =    rename_keys(__A)
     _a           =    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_									=    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,
  )



