

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


a_    :						List[str]    		=      logging.get_logger(__name__)


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict		,							_UpperCamelCase		:  List[Any]	)		->							Dict:
 '''simple docstring'''


 try:
  with open(_UpperCamelCase		,							'rb'	) as flax_state_f:
   SCREAMING_SNAKE_CASE								=			from_bytes(_UpperCamelCase		,							flax_state_f.read()	)
 except UnpicklingError as e:
  try:
   with open(_UpperCamelCase	) as f:
    if f.read().startswith('version'	):
     raise OSError(
         'You seem to have cloned a repository without having git-lfs installed. Please'
         ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
         ' folder you cloned.'	)
    else:
     raise ValueError from e
  except (UnicodeDecodeError, ValueError):
   raise EnvironmentError(f"""Unable to convert {model_file} to Flax deserializable object. """	)

 return load_flax_weights_in_pytorch_model(_UpperCamelCase		,							_UpperCamelCase	)




def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  List[Any]	)		->							List[Any]:
 '''simple docstring'''


 try:
  import torch  # noqa: F401
 except ImportError:
  logger.error(
      'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
      ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
      ' instructions.'	)
  raise

 # check if we have bf16 weights
 SCREAMING_SNAKE_CASE								=			flatten_dict(jax.tree_util.tree_map(lambda _UpperCamelCase	: x.dtype == jnp.bfloataa		,							_UpperCamelCase	)	).values()
 if any(_UpperCamelCase	):
  # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

  # and bf16 is not fully supported in PT yet.
  logger.warning(
      'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
      'before loading those in PyTorch model.'	)
  SCREAMING_SNAKE_CASE								=			jax.tree_util.tree_map(
      lambda _UpperCamelCase	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params		,							_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			''

 SCREAMING_SNAKE_CASE								=			flatten_dict(_UpperCamelCase		,							sep='.'	)
 SCREAMING_SNAKE_CASE								=			pt_model.state_dict()

 # keep track of unexpected & missing keys
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			set(pt_model_dict.keys()	)

 for flax_key_tuple, flax_tensor in flax_state_dict.items():
  SCREAMING_SNAKE_CASE								=			flax_key_tuple.split('.'	)

  if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
   SCREAMING_SNAKE_CASE								=			flax_key_tuple_array[:-1] + ['weight']
   SCREAMING_SNAKE_CASE								=			jnp.transpose(_UpperCamelCase		,							(3, 2, 0, 1)	)
  elif flax_key_tuple_array[-1] == "kernel":
   SCREAMING_SNAKE_CASE								=			flax_key_tuple_array[:-1] + ['weight']
   SCREAMING_SNAKE_CASE								=			flax_tensor.T
  elif flax_key_tuple_array[-1] == "scale":
   SCREAMING_SNAKE_CASE								=			flax_key_tuple_array[:-1] + ['weight']

  if "time_embedding" not in flax_key_tuple_array:
   for i, flax_key_tuple_string in enumerate(_UpperCamelCase	):
    SCREAMING_SNAKE_CASE								=			(
        flax_key_tuple_string.replace('_0'		,							'.0'	)
        .replace('_1'		,							'.1'	)
        .replace('_2'		,							'.2'	)
        .replace('_3'		,							'.3'	)
        .replace('_4'		,							'.4'	)
        .replace('_5'		,							'.5'	)
        .replace('_6'		,							'.6'	)
        .replace('_7'		,							'.7'	)
        .replace('_8'		,							'.8'	)
        .replace('_9'		,							'.9'	)
    )

  SCREAMING_SNAKE_CASE								=			'.'.join(_UpperCamelCase	)

  if flax_key in pt_model_dict:
   if flax_tensor.shape != pt_model_dict[flax_key].shape:
    raise ValueError(
        f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
        f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""	)
   else:
    # add weight to pytorch dict
    SCREAMING_SNAKE_CASE								=			np.asarray(_UpperCamelCase	) if not isinstance(_UpperCamelCase		,							np.ndarray	) else flax_tensor
    SCREAMING_SNAKE_CASE								=			torch.from_numpy(_UpperCamelCase	)
    # remove from missing keys
    missing_keys.remove(_UpperCamelCase	)
  else:
   # weight is not expected by PyTorch model
   unexpected_keys.append(_UpperCamelCase	)

 pt_model.load_state_dict(_UpperCamelCase	)

 # re-transform missing_keys to list
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)

 if len(_UpperCamelCase	) > 0:
  logger.warning(
      'Some weights of the Flax model were not used when initializing the PyTorch model'
      f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
      f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
      ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
      f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
      ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
      ' FlaxBertForSequenceClassification model).'	)
 if len(_UpperCamelCase	) > 0:
  logger.warning(
      f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
      f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
      ' use it for predictions and inference.'	)

 return pt_model




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


a_    :						Optional[int]    		=      logging.get_logger(__name__)


a_    :						Union[str, Any]    		=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

a_    :						Any    		=      {
    "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",
    },
}

a_    :						List[Any]    		=      {
    "allenai/led-base-16384": 1_6384,
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         =VOCAB_FILES_NAMES
    __UpperCamelCase         =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase         =LEDTokenizer
    __UpperCamelCase         =["input_ids", "attention_mask"]

    def __init__(			self :      Tuple							,    snake_case__ :      List[Any]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      List[str]=None							,    snake_case__ :      Dict="replace"							,    snake_case__ :      Tuple="<s>"							,    snake_case__ :      Optional[Any]="</s>"							,    snake_case__ :      int="</s>"							,    snake_case__ :      Dict="<s>"							,    snake_case__ :      Union[str, Any]="<unk>"							,    snake_case__ :      Optional[int]="<pad>"							,    snake_case__ :      List[str]="<mask>"							,    snake_case__ :      List[Any]=False							,    snake_case__ :      int=True							,    **snake_case__ :      Dict							,    ):

     """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__							,    )

     SCREAMING_SNAKE_CASE								=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
     if pre_tok_state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
      SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    pre_tok_state.pop('type'    )    )
      SCREAMING_SNAKE_CASE								=			add_prefix_space
      SCREAMING_SNAKE_CASE								=			pre_tok_class(**snake_case__    )

     SCREAMING_SNAKE_CASE								=			add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     SCREAMING_SNAKE_CASE								=			'post_processor'
     SCREAMING_SNAKE_CASE								=			getattr(self.backend_tokenizer							,    snake_case__							,    snake_case__    )
     if tokenizer_component_instance:
      SCREAMING_SNAKE_CASE								=			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:
       SCREAMING_SNAKE_CASE								=			tuple(state['sep']    )
      if "cls" in state:
       SCREAMING_SNAKE_CASE								=			tuple(state['cls']    )

      SCREAMING_SNAKE_CASE								=			False

      if state.get('add_prefix_space'							,    snake_case__    ) != add_prefix_space:
       SCREAMING_SNAKE_CASE								=			add_prefix_space
       SCREAMING_SNAKE_CASE								=			True

      if state.get('trim_offsets'							,    snake_case__    ) != trim_offsets:
       SCREAMING_SNAKE_CASE								=			trim_offsets
       SCREAMING_SNAKE_CASE								=			True

      if changes_to_apply:
       SCREAMING_SNAKE_CASE								=			getattr(snake_case__							,    state.pop('type'    )    )
       SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Optional[Any]    ):

     """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 UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AddedToken(snake_case__							,    lstrip=snake_case__							,    rstrip=snake_case__    ) if isinstance(snake_case__							,    snake_case__    ) else value
     SCREAMING_SNAKE_CASE								=			value

    def UpperCamelCase			(			self :      Dict							,    *snake_case__ :      Optional[Any]							,    **snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      List[str]							,    *snake_case__ :      List[Any]							,    **snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      str							,    snake_case__ :      Optional[str] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self._tokenizer.model.save(snake_case__							,    name=snake_case__    )
     return tuple(snake_case__    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      List[int]							,    snake_case__ :      Optional[List[int]] = None    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			[self.sep_token_id]
     SCREAMING_SNAKE_CASE								=			[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 UpperCamelCase			(			self :      Optional[Any]							,    snake_case__ :      Union[Dict[str, EncodedInput], BatchEncoding]							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,    snake_case__ :      Optional[int] = None							,    snake_case__ :      Optional[bool] = None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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:
      SCREAMING_SNAKE_CASE								=			'attention_mask' in self.model_input_names

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

      if needs_to_be_padded:
       SCREAMING_SNAKE_CASE								=			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`
        SCREAMING_SNAKE_CASE								=			(
            encoded_inputs['global_attention_mask'] + [-1] * difference
        )
       elif self.padding_side == "left":
        SCREAMING_SNAKE_CASE								=			[-1] * difference + encoded_inputs[
            'global_attention_mask'
        ]
       else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side    )    )

     return encoded_inputs

