'''simple docstring'''





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Dict    	=       dataset
         SCREAMING_SNAKE_CASE					:			Any    	=       process
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       params






    def __len__(       self )							-> List[Any]:
         return len(self.dataset )






    def __getitem__(       self    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.dataset[i]
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.process(a_    ,							**self.params )
         return processed






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=None )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			int    	=       loader
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       infer
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       params
         if loader_batch_size == 1:
              # Let's spare some time by deactivating altogether
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       loader_batch_size

         # Internal bookkeeping
         SCREAMING_SNAKE_CASE					:			str    	=       None
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       None






    def __len__(       self )							-> List[Any]:
         return len(self.loader )






    def __iter__(       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Any    	=       iter(self.loader )
         return self






    def       _UpperCamelCase    (       self )							-> str:
         if isinstance(self._loader_batch_data    ,							torch.Tensor ):
              # Batch data is simple tensor, just fetch the slice
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._loader_batch_data[self._loader_batch_index]
         else:
              # Batch data is assumed to be BaseModelOutput (or dict)
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}
              for k, element in self._loader_batch_data.items():
                   if isinstance(a_    ,							a_ ):
                        # Convert ModelOutput to tuple first
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       element.to_tuple()
                        if isinstance(element[0]    ,							torch.Tensor ):
                             SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
                        elif isinstance(element[0]    ,							np.ndarray ):
                             SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tuple(np.expand_dims(el[self._loader_batch_index]    ,							0 ) for el in element )
                        continue
                   if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(a_    ,							a_ ):
                        # Those are stored as lists of tensors so need specific unbatching.
                        if isinstance(element[0]    ,							torch.Tensor ):
                             SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
                        elif isinstance(element[0]    ,							np.ndarray ):
                             SCREAMING_SNAKE_CASE					:			Tuple    	=       tuple(np.expand_dims(el[self._loader_batch_index]    ,							0 ) for el in element )
                        continue
                   if element is None:
                        # This can happen for optional data that get passed around
                        SCREAMING_SNAKE_CASE					:			Dict    	=       None
                   elif isinstance(element[self._loader_batch_index]    ,							torch.Tensor ):
                        # Take correct batch data, but make it looked like batch_size=1
                        # For compatibility with other methods within transformers

                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       element[self._loader_batch_index].unsqueeze(0 )
                   elif isinstance(element[self._loader_batch_index]    ,							np.ndarray ):
                        # Take correct batch data, but make it looked like batch_size=1
                        # For compatibility with other methods within transformers
                        SCREAMING_SNAKE_CASE					:			Any    	=       np.expand_dims(element[self._loader_batch_index]    ,							0 )
                   else:
                        # This is typically a list, so no need to `unsqueeze`.
                        SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._loader_batch_data.__class__(a_ )
         self._loader_batch_index += 1
         return result






    def       _UpperCamelCase    (       self )							-> List[str]:
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
              # We are currently unrolling a batch so we just need to return
              # the current item within a batch
              return self.loader_batch_item()

         # We're out of items within a batch
         SCREAMING_SNAKE_CASE					:			str    	=       next(self.iterator )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.infer(a_    ,							**self.params )
         # We now have a batch of "inferred things".
         if self.loader_batch_size is not None:
              # Try to infer the size of the batch
              if isinstance(a_    ,							torch.Tensor ):
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       processed
              else:
                   SCREAMING_SNAKE_CASE					:			str    	=       list(processed.keys() )[0]
                   SCREAMING_SNAKE_CASE					:			Any    	=       processed[key]
              if isinstance(a_    ,							a_ ):
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       len(a_ )
              else:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       first_tensor.shape[0]
              if 0 < observed_batch_size < self.loader_batch_size:
                   # could be last batch so we can't unroll as many
                   # elements.
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       observed_batch_size
              # Setting internal index to unwrap the batch
              SCREAMING_SNAKE_CASE					:			Any    	=       processed
              SCREAMING_SNAKE_CASE					:			Dict    	=       0
              return self.loader_batch_item()
         else:
              # We're not unrolling batches
              return processed






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=None )							-> Dict:
         super().__init__(a_    ,							a_    ,							a_ )






    def __iter__(       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       iter(self.loader )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       None
         return self






    def       _UpperCamelCase    (       self )							-> Any:
         if self.subiterator is None:
              SCREAMING_SNAKE_CASE					:			Any    	=       self.infer(next(self.iterator )    ,							**self.params )
         try:
              # Try to return next item
              SCREAMING_SNAKE_CASE					:			str    	=       next(self.subiterator )
         except StopIteration:
              # When a preprocess iterator ends, we can start lookig at the next item
              # ChunkIterator will keep feeding until ALL elements of iterator
              # all have created their subiterator and have been iterating against.
              #
              # Another way to look at it, is we're basically flattening lists of lists
              # into a single list, but with generators
              SCREAMING_SNAKE_CASE					:			Tuple    	=       self.infer(next(self.iterator )    ,							**self.params )
              SCREAMING_SNAKE_CASE					:			Dict    	=       next(self.subiterator )
         return processed






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __iter__(       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Dict    	=       iter(self.loader )
         return self






    def       _UpperCamelCase    (       self )							-> Tuple:
         # Extremely similar to PipelineIterator in its unpacking mechanism
         # BUT, we have an extra required item which is the presence of `is_last`
         # That is because everything is flattened by `PipelineChunkIterator` we
         # need to keep track of how to regroup here in the original `process`
         # boundaries so that `process` and `postprocess` see the same data.

         # This iterator accumulates items (possibly while unbatching) until it
         # its a `is_last` and then just passes it on to the caller.
         SCREAMING_SNAKE_CASE					:			Tuple    	=       False
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       []
         if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
              while self._loader_batch_index < self.loader_batch_size:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.loader_batch_item()
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       item.pop('is_last' )
                   accumulator.append(a_ )
                   if is_last:
                        return accumulator

         while not is_last:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       self.infer(next(self.iterator )    ,							**self.params )
              if self.loader_batch_size is not None:
                   if isinstance(a_    ,							torch.Tensor ):
                        SCREAMING_SNAKE_CASE					:			List[str]    	=       processed
                   else:
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       list(processed.keys() )[0]
                        SCREAMING_SNAKE_CASE					:			Any    	=       processed[key]
                   if isinstance(a_    ,							a_ ):
                        SCREAMING_SNAKE_CASE					:			List[str]    	=       len(a_ )
                   else:
                        SCREAMING_SNAKE_CASE					:			int    	=       first_tensor.shape[0]
                   if 0 < observed_batch_size < self.loader_batch_size:
                        # could be last batch so we can't unroll as many
                        # elements.
                        SCREAMING_SNAKE_CASE					:			str    	=       observed_batch_size
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       processed
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       0
                   while self._loader_batch_index < self.loader_batch_size:
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.loader_batch_item()
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       item.pop('is_last' )
                        accumulator.append(a_ )
                        if is_last:
                             return accumulator
              else:
                   SCREAMING_SNAKE_CASE					:			Any    	=       processed
                   SCREAMING_SNAKE_CASE					:			Dict    	=       item.pop('is_last' )
                   accumulator.append(a_ )
         return accumulator






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       dataset
         SCREAMING_SNAKE_CASE					:			Dict    	=       key






    def __len__(       self )							-> Tuple:
         return len(self.dataset )






    def __getitem__(       self    ,							lowercase__ )							-> Optional[int]:
         return self.dataset[i][self.key]






class 				UpperCAmelCase (       __lowerCamelCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dataset
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       keya
         SCREAMING_SNAKE_CASE					:			str    	=       keya






    def __len__(       self )							-> List[Any]:
         return len(self.dataset )






    def __getitem__(       self    ,							lowercase__ )							-> Dict:
         return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



'''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  :int  				=    logging.get_logger(__name__)


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

_lowerCAmelCase  :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  :str  				=    {
    """allenai/led-base-16384""": 16_384,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Union[str, Any]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						LEDTokenizer
    snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]






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

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

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       add_prefix_space

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

              SCREAMING_SNAKE_CASE					:			str    	=       False

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

              if state.get('trim_offsets'    ,							lowercase__ ) != trim_offsets:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       trim_offsets
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							state.pop('type' ) )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       component_class(**lowercase__ )
                   setattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )






    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def       _UpperCamelCase    (       self )							-> str:
         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    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else value
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       value






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			Dict    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._tokenizer.model.save(lowercase__    ,							name=lowercase__ )
         return tuple(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       [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    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.sep_token_id]
         SCREAMING_SNAKE_CASE					:			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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = PaddingStrategy.DO_NOT_PAD    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> dict:
         SCREAMING_SNAKE_CASE					:			int    	=       super()._pad(
             encoded_inputs=lowercase__    ,							max_length=lowercase__    ,							padding_strategy=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_attention_mask=lowercase__    ,							)

         # Load from model defaults
         if return_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       'attention_mask' in self.model_input_names

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

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

         return encoded_inputs



