





"""simple docstring"""




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)


lowerCamelCase_     :						Optional[Any]						=					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase_     :						Optional[int]						=					{
    '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_     :						Optional[Any]						=					{
    'allenai/led-base-16384': 1_63_84,
}

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Union[str, Any]	    =   VOCAB_FILES_NAMES
      lowercase_      : Union[str, Any]	    =   PRETRAINED_VOCAB_FILES_MAP
      lowercase_      : Union[str, Any]	    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase_      : Any	    =   LEDTokenizer
      lowercase_      : List[Any]	    =   ["""input_ids""", """attention_mask"""]

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      snake_case_=None					,      snake_case_="replace"					,      snake_case_="<s>"					,      snake_case_="</s>"					,      snake_case_="</s>"					,      snake_case_="<s>"					,      snake_case_="<unk>"					,      snake_case_="<pad>"					,      snake_case_="<mask>"					,      snake_case_=False					,      snake_case_=True					,      **snake_case_					,      ):

        """simple docstring"""

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

        A_  :       str										=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
        if pre_tok_state.get('add_prefix_space'					,      snake_case_  ) != add_prefix_space:
          A_  :       Any										=  getattr(snake_case_					,      pre_tok_state.pop('type'  )  )
          A_  :       Optional[int]										=  add_prefix_space
          A_  :       Dict										=  pre_tok_class(**snake_case_  )

        A_  :       List[str]										=  add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A_  :       Optional[Any]										=  'post_processor'
        A_  :       Tuple										=  getattr(self.backend_tokenizer					,      snake_case_					,      snake_case_  )
        if tokenizer_component_instance:
          A_  :       Tuple										=  json.loads(tokenizer_component_instance.__getstate__()  )

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
            A_  :       int										=  tuple(state['sep']  )
          if "cls" in state:
            A_  :       Any										=  tuple(state['cls']  )

          A_  :       Optional[Any]										=  False

          if state.get('add_prefix_space'					,      snake_case_  ) != add_prefix_space:
            A_  :       Dict										=  add_prefix_space
            A_  :       Optional[int]										=  True

          if state.get('trim_offsets'					,      snake_case_  ) != trim_offsets:
            A_  :       Optional[Any]										=  trim_offsets
            A_  :       List[Any]										=  True

          if changes_to_apply:
            A_  :       Tuple										=  getattr(snake_case_					,      state.pop('type'  )  )
            A_  :       Tuple										=  component_class(**snake_case_  )
            setattr(self.backend_tokenizer					,      snake_case_					,      snake_case_  )

      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self._mask_token is None:
          if self.verbose:
            logger.error('Using mask_token, but it is not set yet.'  )
          return None
        return str(self._mask_token  )

      @mask_token.setter
      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       str										=  AddedToken(snake_case_					,      lstrip=snake_case_					,      rstrip=snake_case_  ) if isinstance(snake_case_					,      snake_case_  ) else value
        A_  :       Optional[Any]										=  value

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Any										=  kwargs.get('is_split_into_words'					,      snake_case_  )

        if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              'to use it with pretokenized inputs.'  )

        return super()._batch_encode_plus(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  kwargs.get('is_split_into_words'					,      snake_case_  )

        if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
              'to use it with pretokenized inputs.'  )

        return super()._encode_plus(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  self._tokenizer.model.save(snake_case_					,      name=snake_case_  )
        return tuple(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None  ):

        """simple docstring"""

        A_  :       Optional[int]										=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
          return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        A_  :       List[Any]										=  [self.sep_token_id]
        A_  :       Optional[Any]										=  [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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = PaddingStrategy.DO_NOT_PAD					,      snake_case_ = None					,      snake_case_ = None					,      ):

        """simple docstring"""

        A_  :       Dict										=  super()._pad(
            encoded_inputs=snake_case_					,      max_length=snake_case_					,      padding_strategy=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_attention_mask=snake_case_					,      )

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

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

          if needs_to_be_padded:
            A_  :       str										=  len(snake_case_  ) - 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_  :       str										=  (
                  encoded_inputs['global_attention_mask'] + [-1] * difference
              )
            elif self.padding_side == "left":
              A_  :       Optional[int]										=  [-1] * difference + encoded_inputs[
                  'global_attention_mask'
              ]
            else:
              raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

        return encoded_inputs






"""simple docstring"""




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  R'\w+[.]\d+'
  A_  :       int										=  re.findall(_UpperCAmelCase ,					_UpperCAmelCase					)
  for pat in pats:
    A_  :       Optional[int]										=  key.replace(_UpperCAmelCase ,					'_'.join(pat.split('.'					)					)					)
  return key





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  pt_tuple_key[:-1] + ('scale',)
  if (
      any('norm' in str_ for str_ in pt_tuple_key					)
      and (pt_tuple_key[-1] == "bias")
      and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
      and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
  ):
    A_  :       Union[str, Any]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor
  elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
    A_  :       List[str]										=  pt_tuple_key[:-1] + ('scale',)
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
    A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('embedding',)
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  A_  :       int										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
    A_  :       str										=  pt_tensor.transpose(2 ,					3 ,					1 ,					0					)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  A_  :       Optional[Any]										=  pt_tuple_key[:-1] + ('kernel',)
  if pt_tuple_key[-1] == "weight":
    A_  :       Optional[Any]										=  pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  A_  :       Tuple										=  pt_tuple_key[:-1] + ('weight',)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  A_  :       Optional[int]										=  pt_tuple_key[:-1] + ('bias',)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase=42					):






  """simple docstring"""





  A_  :       int										=  {k: v.numpy() for k, v in pt_state_dict.items()}

  # Step 2: Since the model is stateless, get random Flax params
  A_  :       Union[str, Any]										=  flax_model.init_weights(PRNGKey(_UpperCAmelCase					)					)

  A_  :       Optional[Any]										=  flatten_dict(_UpperCAmelCase					)
  A_  :       Tuple										=  {}

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    A_  :       Any										=  rename_key(_UpperCAmelCase					)
    A_  :       List[str]										=  tuple(renamed_pt_key.split('.'					)					)

    # Correctly rename weight parameters
    A_						,       A_  :       Union[str, Any]										=  rename_key_and_reshape_tensor(_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					)

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
            f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)

        # also add unexpected weight so that warning is thrown
    A_  :       str										=  jnp.asarray(_UpperCAmelCase					)

  return unflatten_dict(_UpperCAmelCase					)