






'''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


__UpperCamelCase   :							Dict         =   logging.get_logger(__name__)

__UpperCamelCase   :							Optional[int]         =   {
    """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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="gpt_neo"
      __a	       =["past_key_values"]
      __a	       ={"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




      def __init__(	self			, lowerCamelCase=5_0257			, lowerCamelCase=2048			, lowerCamelCase=2048			, lowerCamelCase=24			, lowerCamelCase=[[["global", "local"], 12]]			, lowerCamelCase=16			, lowerCamelCase=None			, lowerCamelCase=256			, lowerCamelCase="gelu_new"			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.1			, lowerCamelCase=1e-5			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, **lowerCamelCase			, )				->int:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     max_position_embeddings
         __a					  =     hidden_size
         __a					  =     num_layers
         __a					  =     num_heads
         __a					  =     intermediate_size
         __a					  =     window_size
         __a					  =     activation_function
         __a					  =     resid_dropout
         __a					  =     embed_dropout
         __a					  =     attention_dropout
         __a					  =     classifier_dropout
         __a					  =     layer_norm_epsilon
         __a					  =     initializer_range
         __a					  =     use_cache

         __a					  =     bos_token_id
         __a					  =     eos_token_id

         __a					  =     attention_types
         __a					  =     self.expand_attention_types_params(lowerCamelCase   )

         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=lowerCamelCase			, eos_token_id=lowerCamelCase			, **lowerCamelCase   )






      @staticmethod
      def 			__UpperCamelCase				(	lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         __a					  =     []
         for item in attention_types:
            for _ in range(item[1]   ):
               attentions.extend(item[0]   )
         return attentions







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						int     )	->      Any:

   """simple docstring"""
   import torch

   __a					  =     input.size()
   __a					  =     len(SCREAMING_SNAKE_CASE__     )
   __a					  =     shape[dimension]

   __a					  =     torch.arange(0,       SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     torch.div(sizedim - size,       SCREAMING_SNAKE_CASE__,       rounding_mode='floor'     ) + 1
   __a					  =     torch.arange(SCREAMING_SNAKE_CASE__     ) + low_indices[:min_length][:, None]

   __a					  =     [slice(SCREAMING_SNAKE_CASE__     )] * rank
   __a					  =     indices
   __a					  =     input[s]

   __a					  =     list(range(0,       rank + 1     )     )
   perm.append(perm.pop(dimension + 1     )     )

   return sliced.permute(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      List[str]:

   """simple docstring"""
   import torch

   __a					  =     torch.arange(1,       SCREAMING_SNAKE_CASE__     )
   __a					  =     torch.remainder(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
   __a					  =     remainders == 0
   __a					  =     candidates[divisor_indices]
   __a					  =     torch.max(SCREAMING_SNAKE_CASE__     )
   return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__,       rounding_mode='floor'     )



class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )
            __a					  =     {0: 'batch', 1: 'past_sequence + sequence'}
         else:
            __a					  =     {0: 'batch', 1: 'sequence'}

         return common_inputs




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return self._config.num_heads




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     super(lowerCamelCase			, self   ).generate_dummy_inputs(
             lowerCamelCase			, batch_size=lowerCamelCase			, seq_length=lowerCamelCase			, is_pair=lowerCamelCase			, framework=lowerCamelCase   )

         # We need to order the input in the way they appears in the forward()
         __a					  =     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

               __a  ,      __a					  =     common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               __a					  =     seqlen + 2
               __a					  =     (
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               __a					  =     [
                   (torch.zeros(lowerCamelCase   ), torch.zeros(lowerCamelCase   )) for _ in range(self.num_layers   )
               ]

         __a					  =     common_inputs['attention_mask']
         if self.use_past:
            __a					  =     ordered_inputs['attention_mask'].dtype
            __a					  =     torch.cat(
                [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase			, lowerCamelCase			, dtype=lowerCamelCase   )]			, dim=1   )

         return ordered_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return 13







'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )