

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


snake_case_        =			logging.get_logger(__name__)


snake_case_        =			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_        =			{
    '''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''',
    },
}

snake_case_        =			{
    '''allenai/led-base-16384''': 16_384,
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	Union[str, Any]														=					VOCAB_FILES_NAMES
    __lowerCamelCase						:	Union[str, Any]														=					PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:	List[str]														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:	Any														=					LEDTokenizer
    __lowerCamelCase						:	str														=					["""input_ids""", """attention_mask"""]





    def __init__(				self						,       a=None						,       a=None						,       a=None						,       a="replace"						,       a="<s>"						,       a="</s>"						,       a="</s>"						,       a="<s>"						,       a="<unk>"						,       a="<pad>"						,       a="<mask>"						,       a=False						,       a=True						,       **a						,       ):
         super().__init__(
             a						,       a						,       tokenizer_file=a						,       errors=a						,       bos_token=a						,       eos_token=a						,       sep_token=a						,       cls_token=a						,       unk_token=a						,       pad_token=a						,       mask_token=a						,       add_prefix_space=a						,       trim_offsets=a						,       **a						,       )

         lowercase__   :       Union[str, Any]				=	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get('add_prefix_space'						,       a) != add_prefix_space:
              lowercase__   :       Tuple				=	getattr(a						,       pre_tok_state.pop('type'))
              lowercase__   :       List[str]				=	add_prefix_space
              lowercase__   :       Tuple				=	pre_tok_class(**a)

         lowercase__   :       Any				=	add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         lowercase__   :       List[Any]				=	'post_processor'
         lowercase__   :       List[Any]				=	getattr(self.backend_tokenizer						,       a						,       a)
         if tokenizer_component_instance:
              lowercase__   :       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:
                   lowercase__   :       Union[str, Any]				=	tuple(state['sep'])
              if "cls" in state:
                   lowercase__   :       List[str]				=	tuple(state['cls'])

              lowercase__   :       Dict				=	False

              if state.get('add_prefix_space'						,       a) != add_prefix_space:
                   lowercase__   :       int				=	add_prefix_space
                   lowercase__   :       str				=	True

              if state.get('trim_offsets'						,       a) != trim_offsets:
                   lowercase__   :       Any				=	trim_offsets
                   lowercase__   :       Tuple				=	True

              if changes_to_apply:
                   lowercase__   :       Union[str, Any]				=	getattr(a						,       state.pop('type'))
                   lowercase__   :       List[Any]				=	component_class(**a)
                   setattr(self.backend_tokenizer						,       a						,       a)





    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def 				snake_case_    (				self):
         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 				snake_case_    (				self						,       a):
         lowercase__   :       str				=	AddedToken(a						,       lstrip=a						,       rstrip=a) if isinstance(a						,       a) else value
         lowercase__   :       Dict				=	value





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       List[str]				=	kwargs.get('is_split_into_words'						,       a)

         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(*a						,       **a)





    def 				snake_case_    (				self						,       *a						,       **a):
         lowercase__   :       Any				=	kwargs.get('is_split_into_words'						,       a)

         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(*a						,       **a)





    def 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       Any				=	self._tokenizer.model.save(a						,       name=a)
         return tuple(a)





    def 				snake_case_    (				self						,       a						,       a=None):
         lowercase__   :       Union[str, Any]				=	[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 				snake_case_    (				self						,       a						,       a = None):
         lowercase__   :       int				=	[self.sep_token_id]
         lowercase__   :       str				=	[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						,       a						,       a = None						,       a = PaddingStrategy.DO_NOT_PAD						,       a = None						,       a = None						,       ):
         lowercase__   :       List[Any]				=	super()._pad(
             encoded_inputs=a						,       max_length=a						,       padding_strategy=a						,       pad_to_multiple_of=a						,       return_attention_mask=a						,       )

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

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

              if needs_to_be_padded:
                   lowercase__   :       Union[str, Any]				=	len(a) - 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`
                        lowercase__   :       str				=	(
                            encoded_inputs['global_attention_mask'] + [-1] * difference
                        )
                   elif self.padding_side == "left":
                        lowercase__   :       List[Any]				=	[-1] * difference + encoded_inputs[
                            'global_attention_mask'
                        ]
                   else:
                        raise ValueError('Invalid padding strategy:' + str(self.padding_side))

         return encoded_inputs




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   BertModel     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Tuple				=	('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

     lowercase__   :       Dict				=	(
         ('layer.', 'layer_'),
         ('word_embeddings.weight', 'word_embeddings'),
         ('position_embeddings.weight', 'position_embeddings'),
         ('token_type_embeddings.weight', 'token_type_embeddings'),
         ('.', '/'),
         ('LayerNorm/weight', 'LayerNorm/gamma'),
         ('LayerNorm/bias', 'LayerNorm/beta'),
         ('weight', 'kernel'),
     )

     if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
          os.makedirs(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       str				=	model.state_dict()

     def to_tf_var_name(SCREAMING_SNAKE_CASE_		:   str      ):
          for patt, repl in iter(SCREAMING_SNAKE_CASE_      ):
               lowercase__   :       Optional[int]				=	name.replace(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
          return f"""bert/{name}"""

     def create_tf_var(SCREAMING_SNAKE_CASE_		:   np.ndarray     ,					SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   tf.Session      ):
          lowercase__   :       Optional[Any]				=	tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__   :       List[str]				=	tf.get_variable(dtype=SCREAMING_SNAKE_CASE_     ,					shape=tensor.shape     ,					name=SCREAMING_SNAKE_CASE_     ,					initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(SCREAMING_SNAKE_CASE_      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__   :       Tuple				=	to_tf_var_name(SCREAMING_SNAKE_CASE_      )
               lowercase__   :       str				=	state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__   :       str				=	torch_tensor.T
               lowercase__   :       Any				=	create_tf_var(tensor=SCREAMING_SNAKE_CASE_     ,					name=SCREAMING_SNAKE_CASE_     ,					session=SCREAMING_SNAKE_CASE_      )
               tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	session.run(SCREAMING_SNAKE_CASE_      )
               print(f"""Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )}"""      )

          lowercase__   :       Tuple				=	tf.train.Saver(tf.trainable_variables()      )
          saver.save(SCREAMING_SNAKE_CASE_     ,					os.path.join(SCREAMING_SNAKE_CASE_     ,					model_name.replace('-'     ,					'_'      ) + '.ckpt'      )      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[Any]=None      ):



     '''simple docstring'''





     lowercase__   :       List[str]				=	argparse.ArgumentParser()
     parser.add_argument('--model_name'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='model name e.g. bert-base-uncased'      )
     parser.add_argument(
         '--cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					default=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory containing pytorch model'      )
     parser.add_argument('--pytorch_model_path'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='/path/to/<pytorch-model-name>.bin'      )
     parser.add_argument('--tf_cache_dir'     ,					type=SCREAMING_SNAKE_CASE_     ,					required=SCREAMING_SNAKE_CASE_     ,					help='Directory in which to save tensorflow model'      )
     lowercase__   :       List[str]				=	parser.parse_args(SCREAMING_SNAKE_CASE_      )

     lowercase__   :       int				=	BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name     ,					state_dict=torch.load(args.pytorch_model_path      )     ,					cache_dir=args.cache_dir     ,					)

     convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_     ,					ckpt_dir=args.tf_cache_dir     ,					model_name=args.model_name      )


if __name__ == "__main__":
       main()

