





'''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 :					Union[str, Any] 							=logging.get_logger(__name__)


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

lowerCAmelCase :					List[str] 							={
    '''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 :					Dict 							={
    '''allenai/led-base-16384''': 16_384,
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      VOCAB_FILES_NAMES
     __A								=      PRETRAINED_VOCAB_FILES_MAP
     __A								=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A								=      LEDTokenizer
     __A								=      ["input_ids", "attention_mask"]





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





        """simple docstring"""




        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     ,				)

        lowercase_      :Any         =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"     ,				lowercase						) != add_prefix_space:
           lowercase_      :List[str]         =			getattr(lowercase     ,				pre_tok_state.pop("type"						)						)
           lowercase_      :Optional[Any]         =			add_prefix_space
           lowercase_      :Optional[int]         =			pre_tok_class(**lowercase						)

        lowercase_      :str         =			add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_      :Any         =			"post_processor"
        lowercase_      :Optional[int]         =			getattr(self.backend_tokenizer     ,				lowercase     ,				lowercase						)
        if tokenizer_component_instance:
           lowercase_      :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:
              lowercase_      :Optional[int]         =			tuple(state["sep"]						)
           if "cls" in state:
              lowercase_      :str         =			tuple(state["cls"]						)

           lowercase_      :Tuple         =			False

           if state.get("add_prefix_space"     ,				lowercase						) != add_prefix_space:
              lowercase_      :List[str]         =			add_prefix_space
              lowercase_      :str         =			True

           if state.get("trim_offsets"     ,				lowercase						) != trim_offsets:
              lowercase_      :Dict         =			trim_offsets
              lowercase_      :Any         =			True

           if changes_to_apply:
              lowercase_      :Dict         =			getattr(lowercase     ,				state.pop("type"						)						)
              lowercase_      :Tuple         =			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 							lowercase__				(			self		: Optional[int]						):





        """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 							lowercase__				(			self		: Tuple     ,				lowercase		: Dict						):





        """simple docstring"""




        lowercase_      :Tuple         =			AddedToken(lowercase     ,				lstrip=lowercase     ,				rstrip=lowercase						) if isinstance(lowercase     ,				lowercase						) else value
        lowercase_      :Tuple         =			value





     def 							lowercase__				(			self		: List[Any]     ,				*lowercase		: Tuple     ,				**lowercase		: str						):





        """simple docstring"""




        lowercase_      :Any         =			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 							lowercase__				(			self		: Tuple     ,				*lowercase		: Optional[Any]     ,				**lowercase		: Dict						):





        """simple docstring"""




        lowercase_      :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 							lowercase__				(			self		: Dict     ,				lowercase		: str     ,				lowercase		: Optional[str] = None						):





        """simple docstring"""




        lowercase_      :Any         =			self._tokenizer.model.save(lowercase     ,				name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__				(			self		: Any     ,				lowercase		: Optional[Any]     ,				lowercase		: Any=None						):





        """simple docstring"""




        lowercase_      :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 							lowercase__				(			self		: Dict     ,				lowercase		: List[int]     ,				lowercase		: Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_      :Dict         =			[self.sep_token_id]
        lowercase_      :Union[str, 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 							lowercase__				(			self		: Optional[int]     ,				lowercase		: Union[Dict[str, EncodedInput], BatchEncoding]     ,				lowercase		: Optional[int] = None     ,				lowercase		: PaddingStrategy = PaddingStrategy.DO_NOT_PAD     ,				lowercase		: Optional[int] = None     ,				lowercase		: Optional[bool] = None     ,				):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			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:
           lowercase_      :Tuple         =			"attention_mask" in self.model_input_names

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

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

        return encoded_inputs









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase :					Union[str, Any] 							=logging.get_logger(__name__)

lowerCAmelCase :					Optional[Any] 							={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "gpt_neo"
     __A								=      ["past_key_values"]
     __A								=      {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





     def __init__(			self		: Union[str, Any]     ,				lowercase		: Tuple=50_257     ,				lowercase		: Optional[Any]=2_048     ,				lowercase		: Union[str, Any]=2_048     ,				lowercase		: int=24     ,				lowercase		: Optional[Any]=[[["global", "local"], 12]]     ,				lowercase		: List[Any]=16     ,				lowercase		: List[str]=None     ,				lowercase		: Union[str, Any]=256     ,				lowercase		: Optional[Any]="gelu_new"     ,				lowercase		: Any=0.0     ,				lowercase		: List[Any]=0.0     ,				lowercase		: Any=0.0     ,				lowercase		: str=0.1     ,				lowercase		: Dict=1e-5     ,				lowercase		: List[str]=0.02     ,				lowercase		: Union[str, Any]=True     ,				lowercase		: int=50_256     ,				lowercase		: Union[str, Any]=50_256     ,				**lowercase		: Dict     ,				):





        """simple docstring"""




        lowercase_      :str         =			vocab_size
        lowercase_      :Tuple         =			max_position_embeddings
        lowercase_      :Tuple         =			hidden_size
        lowercase_      :List[str]         =			num_layers
        lowercase_      :int         =			num_heads
        lowercase_      :Union[str, Any]         =			intermediate_size
        lowercase_      :Tuple         =			window_size
        lowercase_      :Any         =			activation_function
        lowercase_      :Tuple         =			resid_dropout
        lowercase_      :Any         =			embed_dropout
        lowercase_      :str         =			attention_dropout
        lowercase_      :List[str]         =			classifier_dropout
        lowercase_      :List[Any]         =			layer_norm_epsilon
        lowercase_      :List[str]         =			initializer_range
        lowercase_      :int         =			use_cache

        lowercase_      :Tuple         =			bos_token_id
        lowercase_      :Optional[Any]         =			eos_token_id

        lowercase_      :int         =			attention_types
        lowercase_      :Tuple         =			self.expand_attention_types_params(lowercase						)

        if len(self.attention_layers						) != self.num_layers:
           raise ValueError(
               "Configuration for convolutional module is incorrect. "
               "It is required that `len(config.attention_layers)` == `config.num_layers` "
               F'but is `len(config.attention_layers) = {len(self.attention_layers						)}`, '
               F'`config.num_layers = {self.num_layers}`. '
               "`config.attention_layers` is prepared using `config.attention_types`. "
               "Please verify the value of `config.attention_types` argument."						)

        super().__init__(bos_token_id=lowercase     ,				eos_token_id=lowercase     ,				**lowercase						)







     @staticmethod
     def 							lowercase__				(			lowercase		: str						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			[]
        for item in attention_types:
           for _ in range(item[1]						):
              attentions.extend(item[0]						)
        return attentions







def 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Optional[int]     ,__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : Dict			):
   import torch

   lowercase_      :List[str]         =			input.size()
   lowercase_      :Union[str, Any]         =			len(__lowerCamelCase			)
   lowercase_      :Any         =			shape[dimension]

   lowercase_      :str         =			torch.arange(0     ,__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Union[str, Any]         =			torch.div(sizedim - size     ,__lowerCamelCase     ,rounding_mode="floor"			) + 1
   lowercase_      :int         =			torch.arange(__lowerCamelCase			) + low_indices[:min_length][:, None]

   lowercase_      :List[Any]         =			[slice(__lowerCamelCase			)] * rank
   lowercase_      :int         =			indices
   lowercase_      :Dict         =			input[s]

   lowercase_      :List[str]         =			list(range(0     ,rank + 1			)			)
   perm.append(perm.pop(dimension + 1			)			)

   return sliced.permute(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]     ,__lowerCamelCase : Any			):
   import torch

   lowercase_      :List[Any]         =			torch.arange(1     ,__lowerCamelCase			)
   lowercase_      :int         =			torch.remainder(__lowerCamelCase     ,__lowerCamelCase			)
   lowercase_      :Optional[int]         =			remainders == 0
   lowercase_      :int         =			candidates[divisor_indices]
   lowercase_      :Tuple         =			torch.max(__lowerCamelCase			)
   return largest_divisor, torch.div(__lowerCamelCase     ,__lowerCamelCase     ,rounding_mode="floor"			)


class 				a_       (    _lowerCAmelCase							):





     @property
     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :int         =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
        if self.use_past:
           self.fill_with_past_key_values_(lowercase     ,				direction="inputs"						)
           lowercase_      :Union[str, Any]         =			{0: "batch", 1: "past_sequence + sequence"}
        else:
           lowercase_      :str         =			{0: "batch", 1: "sequence"}

        return common_inputs





     @property
     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        return self._config.num_heads





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: PreTrainedTokenizer     ,				lowercase		: int = -1     ,				lowercase		: int = -1     ,				lowercase		: bool = False     ,				lowercase		: Optional[TensorType] = None     ,				):





        """simple docstring"""




        lowercase_      :List[str]         =			super(lowercase     ,				self						).generate_dummy_inputs(
            lowercase     ,				batch_size=lowercase     ,				seq_length=lowercase     ,				is_pair=lowercase     ,				framework=lowercase						)

        # We need to order the input in the way they appears in the forward()
        lowercase_      :Tuple         =			OrderedDict({"input_ids": common_inputs["input_ids"]}						)

        # Need to add the past_keys
        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."						)
           else:
              import torch

              lowercase_      ,							lowercase_      :Tuple         =			common_inputs["input_ids"].shape
              # Not using the same length for past_key_values
              lowercase_      :Any         =			seqlen + 2
              lowercase_      :List[Any]         =			(
                  batch,
                  self.num_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // self.num_attention_heads,
              )
              lowercase_      :Dict         =			[
                  (torch.zeros(lowercase						), torch.zeros(lowercase						)) for _ in range(self.num_layers						)
              ]

        lowercase_      :Tuple         =			common_inputs["attention_mask"]
        if self.use_past:
           lowercase_      :Optional[int]         =			ordered_inputs["attention_mask"].dtype
           lowercase_      :List[Any]         =			torch.cat(
               [ordered_inputs["attention_mask"], torch.ones(lowercase     ,				lowercase     ,				dtype=lowercase						)]     ,				dim=1						)

        return ordered_inputs







     @property
     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        return 13


