

'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowerCamelCase		       =    logging.get_logger(__name__)


__lowerCamelCase		       =    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
__lowerCamelCase		       =    {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase		       =    {
    '''allenai/led-base-16384''': 1_6384,
}
@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def       UpperCAmelCase__    (      )			->			Union[str, Any]:
       A_						=      (
           list(range(ord("""!"""				),       ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				),       ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				),       ord("""ÿ"""				) + 1				)				)
       )
       A_						=      bs[:]
       A_						=      0
       for b in range(2**8				):
              if b not in bs:
                     bs.append(UpperCAmelCase__				)
                     cs.append(2**8 + n				)
                     n += 1
       A_						=      [chr(UpperCAmelCase__				) for n in cs]
       return dict(zip(UpperCAmelCase__,       UpperCAmelCase__				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			Optional[int]:
       A_						=      set()
       A_						=      word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)				)
              A_						=      char
       return pairs







class 					A__      (      _snake_case			):
    lowercase						=		VOCAB_FILES_NAMES
    lowercase						=		PRETRAINED_VOCAB_FILES_MAP
    lowercase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase						=		["input_ids", "attention_mask"]



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__="replace"		,				UpperCamelCase__="<s>"		,				UpperCamelCase__="</s>"		,				UpperCamelCase__="</s>"		,				UpperCamelCase__="<s>"		,				UpperCamelCase__="<unk>"		,				UpperCamelCase__="<pad>"		,				UpperCamelCase__="<mask>"		,				UpperCamelCase__=False		,				**UpperCamelCase__		,				)						->      Optional[int]:





           '''simple docstring'''
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else bos_token
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else eos_token
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else sep_token
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else cls_token
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else unk_token
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           A_						=      AddedToken(UpperCamelCase__		,				lstrip=UpperCamelCase__		,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) else mask_token

           super().__init__(
               errors=UpperCamelCase__		,				bos_token=UpperCamelCase__		,				eos_token=UpperCamelCase__		,				unk_token=UpperCamelCase__		,				sep_token=UpperCamelCase__		,				cls_token=UpperCamelCase__		,				pad_token=UpperCamelCase__		,				mask_token=UpperCamelCase__		,				add_prefix_space=UpperCamelCase__		,				**UpperCamelCase__		,				)

           with open(UpperCamelCase__		,				encoding="""utf-8"""       ) as vocab_handle:
                  A_						=      json.load(UpperCamelCase__       )
           A_						=      {v: k for k, v in self.encoder.items()}
           A_						=      errors  # how to handle errors in decoding
           A_						=      bytes_to_unicode()
           A_						=      {v: k for k, v in self.byte_encoder.items()}
           with open(UpperCamelCase__		,				encoding="""utf-8"""       ) as merges_handle:
                  A_						=      merges_handle.read().split("""\n"""       )[1:-1]
           A_						=      [tuple(merge.split()       ) for merge in bpe_merges]
           A_						=      dict(zip(UpperCamelCase__		,				range(len(UpperCamelCase__       )       )       )       )
           A_						=      {}
           A_						=      add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           A_						=      re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )



    @property
    # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           return len(self.encoder       )



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           return dict(self.encoder		,				**self.added_tokens_encoder       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           if token in self.cache:
                  return self.cache[token]
           A_						=      tuple(UpperCamelCase__       )
           A_						=      get_pairs(UpperCamelCase__       )

           if not pairs:
                  return token

           while True:
                  A_						=      min(UpperCamelCase__		,				key=lambda UpperCamelCase__       : self.bpe_ranks.get(UpperCamelCase__		,				float("""inf"""       )       )       )
                  if bigram not in self.bpe_ranks:
                         break
                  A_					,			A_						=      bigram
                  A_						=      []
                  A_						=      0
                  while i < len(UpperCamelCase__       ):
                         try:
                                A_						=      word.index(UpperCamelCase__		,				UpperCamelCase__       )
                         except ValueError:
                                new_word.extend(word[i:]       )
                                break
                         else:
                                new_word.extend(word[i:j]       )
                                A_						=      j

                         if word[i] == first and i < len(UpperCamelCase__       ) - 1 and word[i + 1] == second:
                                new_word.append(first + second       )
                                i += 2
                         else:
                                new_word.append(word[i]       )
                                i += 1
                  A_						=      tuple(UpperCamelCase__       )
                  A_						=      new_word
                  if len(UpperCamelCase__       ) == 1:
                         break
                  else:
                         A_						=      get_pairs(UpperCamelCase__       )
           A_						=      """ """.join(UpperCamelCase__       )
           A_						=      word
           return word



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      []
           for token in re.findall(self.pat		,				UpperCamelCase__       ):
                  A_						=      """""".join(
                      self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                  bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__       ).split(""" """       )       )
           return bpe_tokens



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           return self.encoder.get(UpperCamelCase__		,				self.encoder.get(self.unk_token       )       )



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





           '''simple docstring'''
           return self.decoder.get(UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      """""".join(UpperCamelCase__       )
           A_						=      bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""		,				errors=self.errors       )
           return text



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      Tuple[str]:





           '''simple docstring'''
           if not os.path.isdir(UpperCamelCase__       ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
                  return
           A_						=      os.path.join(
               UpperCamelCase__		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
           A_						=      os.path.join(
               UpperCamelCase__		,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

           with open(UpperCamelCase__		,				"""w"""		,				encoding="""utf-8"""       ) as f:
                  f.write(json.dumps(self.encoder		,				indent=2		,				sort_keys=UpperCamelCase__		,				ensure_ascii=UpperCamelCase__       ) + """\n"""       )

           A_						=      0
           with open(UpperCamelCase__		,				"""w"""		,				encoding="""utf-8"""       ) as writer:
                  writer.write("""#version: 0.2\n"""       )
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,				key=lambda UpperCamelCase__       : kv[1]       ):
                         if index != token_index:
                                logger.warning(
                                    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                    """ Please check that the tokenizer is not corrupted!"""       )
                                A_						=      token_index
                         writer.write(""" """.join(UpperCamelCase__       ) + """\n"""       )
                         index += 1

           return vocab_file, merge_file



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      List[int]:





           '''simple docstring'''
           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           A_						=      [self.cls_token_id]
           A_						=      [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = False       )						->      List[int]:





           '''simple docstring'''
           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCamelCase__		,				token_ids_a=UpperCamelCase__		,				already_has_special_tokens=UpperCamelCase__       )

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase__       )) + [1]
           return [1] + ([0] * len(UpperCamelCase__       )) + [1, 1] + ([0] * len(UpperCamelCase__       )) + [1]



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None       )						->      List[int]:





           '''simple docstring'''
           A_						=      [self.sep_token_id]
           A_						=      [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep       ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__=False		,				**UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      kwargs.pop("""add_prefix_space"""		,				self.add_prefix_space       )
           if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__       ) > 0 and not text[0].isspace()):
                  A_						=      """ """ + text
           return (text, kwargs)







    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__ = None		,				UpperCamelCase__ = PaddingStrategy.DO_NOT_PAD		,				UpperCamelCase__ = None		,				UpperCamelCase__ = None		,				)						->      dict:





           '''simple docstring'''
           A_						=      super()._pad(
               encoded_inputs=UpperCamelCase__		,				max_length=UpperCamelCase__		,				padding_strategy=UpperCamelCase__		,				pad_to_multiple_of=UpperCamelCase__		,				return_attention_mask=UpperCamelCase__		,				)

           # Load from model defaults
           if return_attention_mask is None:
                  A_						=      """attention_mask""" in self.model_input_names

           if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  A_						=      encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  A_						=      len(encoded_inputs["""global_attention_mask"""]       ) != len(UpperCamelCase__       )

                  if needs_to_be_padded:
                         A_						=      len(UpperCamelCase__       ) - len(encoded_inputs["""global_attention_mask"""]       )

                         if self.padding_side == "right":
                                # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                A_						=      (
                                    encoded_inputs["""global_attention_mask"""] + [-1] * difference
                                )
                         elif self.padding_side == "left":
                                A_						=      [-1] * difference + encoded_inputs[
                                    """global_attention_mask"""
                                ]
                         else:
                                raise ValueError("""Invalid padding strategy:""" + str(self.padding_side       )       )

           return encoded_inputs



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

              A_						=      r"""(encoder|decoder)\/"""

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

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

              print(F'''{key} -> {new_key}'''				)
              A_						=      s_dict.pop(UpperCAmelCase__				)

       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(UpperCAmelCase__				):
                            A_						=      expert_weihts[idx]
                            print(F'''{key} -> {key.replace("expert/",       "nested fstring"				)}'''				)

                     s_dict.pop(UpperCAmelCase__				)

       return s_dict


__lowerCamelCase		       =    {
    '''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__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			List[Any]:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(UpperCAmelCase__,       """r"""				) as f:
              A_						=      f.read()

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

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

       A_						=      num_experts
       A_						=      SwitchTransformersConfig(**UpperCAmelCase__				)
       return config
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__=None,       UpperCAmelCase__="./",       UpperCAmelCase__=8				)			->			List[str]:
       # Initialise PyTorch model

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

       if gin_file is not None:
              A_						=      convert_gin_to_config(UpperCAmelCase__,       UpperCAmelCase__				)
       else:
              A_						=      SwitchTransformersConfig.from_pretrained(UpperCAmelCase__				)

       A_						=      SwitchTransformersForConditionalGeneration(UpperCAmelCase__				)

       A_						=      flax_params["""target"""]
       A_						=      flatten_dict(UpperCAmelCase__,       sep="""/"""				)
       A_						=      rename_keys(UpperCAmelCase__				)
       A_						=      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__":
      __lowerCamelCase		       =    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''')
      __lowerCamelCase		       =    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,
      )
