'''simple docstring'''

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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


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

lowerCAmelCase_			:			Optional[Any]    	=   {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}





class 				lowercase    (    snake_case__			):
   lowerCamelCase_            ='blenderbot'
   lowerCamelCase_            =['past_key_values']
   lowerCamelCase_            ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


   def __init__( self					:						int ,		__lowerCAmelCase					:						List[str]=8008 ,		__lowerCAmelCase					:						Dict=128 ,		__lowerCAmelCase					:						Optional[Any]=2 ,		__lowerCAmelCase					:						List[Any]=1_0240 ,		__lowerCAmelCase					:						List[str]=32 ,		__lowerCAmelCase					:						str=24 ,		__lowerCAmelCase					:						Any=1_0240 ,		__lowerCAmelCase					:						Union[str, Any]=32 ,		__lowerCAmelCase					:						Union[str, Any]=0.0 ,		__lowerCAmelCase					:						Optional[int]=0.0 ,		__lowerCAmelCase					:						str=True ,		__lowerCAmelCase					:						Optional[int]=True ,		__lowerCAmelCase					:						Optional[Any]="gelu" ,		__lowerCAmelCase					:						Dict=2560 ,		__lowerCAmelCase					:						Union[str, Any]=0.1 ,		__lowerCAmelCase					:						Any=0.0 ,		__lowerCAmelCase					:						Union[str, Any]=0.0 ,		__lowerCAmelCase					:						Union[str, Any]=0.02 ,		__lowerCAmelCase					:						List[Any]=1 ,		__lowerCAmelCase					:						Optional[int]=False ,		__lowerCAmelCase					:						str=0 ,		__lowerCAmelCase					:						Tuple=1 ,		__lowerCAmelCase					:						List[Any]=2 ,		__lowerCAmelCase					:						Union[str, Any]=3 ,		__lowerCAmelCase					:						int=2 ,		**__lowerCAmelCase					:						Dict ,		)   ->       Optional[int]:
         lowercase_     			=   vocab_size
         lowercase_     			=   max_position_embeddings
         lowercase_     			=   d_model
         lowercase_     			=   encoder_ffn_dim
         lowercase_     			=   encoder_layers
         lowercase_     			=   encoder_attention_heads
         lowercase_     			=   decoder_ffn_dim
         lowercase_     			=   decoder_layers
         lowercase_     			=   decoder_attention_heads
         lowercase_     			=   dropout
         lowercase_     			=   attention_dropout
         lowercase_     			=   activation_dropout
         lowercase_     			=   activation_function
         lowercase_     			=   init_std
         lowercase_     			=   encoder_layerdrop
         lowercase_     			=   decoder_layerdrop
         lowercase_     			=   use_cache
         lowercase_     			=   encoder_layers
         lowercase_     			=   scale_embedding  # scale factor will be sqrt(d_model) if True

         super().__init__(
             pad_token_id=lowercase_ ,		bos_token_id=lowercase_ ,		eos_token_id=lowercase_ ,		is_encoder_decoder=lowercase_ ,		decoder_start_token_id=lowercase_ ,		encoder_no_repeat_ngram_size=lowercase_ ,		forced_eos_token_id=lowercase_ ,		**lowercase_ ,		)





class 				lowercase    (    snake_case__			):


   @property
   def __UpperCAmelCase							( self					:						List[Any])   ->       Union[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     			=   OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ])
               if self.use_past:
                     lowercase_     			=   {0: "batch"}
                     lowercase_     			=   {0: "batch", 1: "past_decoder_sequence + sequence"}
               else:
                     lowercase_     			=   {0: "batch", 1: "decoder_sequence"}
                     lowercase_     			=   {0: "batch", 1: "decoder_sequence"}
               if self.use_past:
                     self.fill_with_past_key_values_(lowercase_ ,		direction="inputs")
         elif self.task == "causal-lm":
               lowercase_     			=   OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ])
               if self.use_past:
                     lowercase_     			=   self.num_layers
                     for i in range(lowercase_):
                           lowercase_     			=   {0: "batch", 2: "past_sequence + sequence"}
                           lowercase_     			=   {0: "batch", 2: "past_sequence + sequence"}
         else:
               lowercase_     			=   OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                       ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                   ])

         return common_inputs


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Optional[int]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     			=   super().outputs
         else:
               lowercase_     			=   super(lowercase_ ,		self).outputs
               if self.use_past:
                     lowercase_     			=   self.num_layers
                     for i in range(lowercase_):
                           lowercase_     			=   {0: "batch", 2: "past_sequence + sequence"}
                           lowercase_     			=   {0: "batch", 2: "past_sequence + sequence"}
         return common_outputs


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						Optional[Any] = -1 ,		__lowerCAmelCase					:						List[Any] = -1 ,		__lowerCAmelCase					:						Tuple = False ,		__lowerCAmelCase					:						List[Any] = None ,		)   ->       str:
         lowercase_     			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_)
         # Generate decoder inputs
         lowercase_     			=   seq_length if not self.use_past else 1
         lowercase_     			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_)
         lowercase_     			=   {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         lowercase_     			=   dict(**lowercase_ ,		**lowercase_)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
               else:
                     import torch
               lowercase_     			=   common_inputs["input_ids"].shape
               lowercase_     			=   common_inputs["decoder_input_ids"].shape[1]
               lowercase_     			=   self.num_attention_heads
               lowercase_     			=   (
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase_     			=   decoder_seq_length
               lowercase_     			=   (
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )
               lowercase_     			=   torch.cat(
                   [common_inputs["decoder_attention_mask"], torch.ones(lowercase_ ,		lowercase_)] ,		dim=1)
               lowercase_     			=   []
               lowercase_     			=   self.num_layers

               for _ in range(lowercase_):
                     common_inputs["past_key_values"].append(
                         (
                             torch.zeros(lowercase_),
                             torch.zeros(lowercase_),
                             torch.zeros(lowercase_),
                             torch.zeros(lowercase_),
                         ))
         return common_inputs


   def __UpperCAmelCase							( self					:						Optional[Any] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						List[str] = -1 ,		__lowerCAmelCase					:						List[Any] = -1 ,		__lowerCAmelCase					:						List[Any] = False ,		__lowerCAmelCase					:						List[Any] = None ,		)   ->       Dict:
         lowercase_     			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_)

         if self.use_past:
               if not is_torch_available():
                     raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
               else:
                     import torch
               lowercase_     			=   common_inputs["input_ids"].shape
               lowercase_     			=   seqlen
               lowercase_     			=   self.num_layers
               lowercase_     			=   self.num_attention_heads
               lowercase_     			=   (
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               lowercase_     			=   common_inputs["attention_mask"].dtype
               lowercase_     			=   torch.cat(
                   [common_inputs["attention_mask"], torch.ones(lowercase_ ,		lowercase_ ,		dtype=lowercase_)] ,		dim=1)
               lowercase_     			=   [
                   (torch.zeros(lowercase_), torch.zeros(lowercase_)) for _ in range(lowercase_)
               ]
         return common_inputs


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Any = -1 ,		__lowerCAmelCase					:						List[str] = -1 ,		__lowerCAmelCase					:						List[Any] = False ,		__lowerCAmelCase					:						Optional[Any] = None ,		)   ->       List[Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowercase_     			=   compute_effective_axis_dimension(
             lowercase_ ,		fixed_dimension=OnnxConfig.default_fixed_batch ,		num_token_to_add=0)

         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase_     			=   tokenizer.num_special_tokens_to_add(lowercase_)
         lowercase_     			=   compute_effective_axis_dimension(
             lowercase_ ,		fixed_dimension=OnnxConfig.default_fixed_sequence ,		num_token_to_add=lowercase_)

         # Generate dummy inputs according to compute batch and sequence
         lowercase_     			=   [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
         lowercase_     			=   dict(tokenizer(lowercase_ ,		return_tensors=lowercase_))
         return common_inputs


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						Union[str, Any] = -1 ,		__lowerCAmelCase					:						Optional[Any] = -1 ,		__lowerCAmelCase					:						Tuple = False ,		__lowerCAmelCase					:						List[str] = None ,		)   ->       Union[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     			=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowercase_ ,		batch_size=lowercase_ ,		seq_length=lowercase_ ,		is_pair=lowercase_ ,		framework=lowercase_)

         elif self.task == "causal-lm":
               lowercase_     			=   self._generate_dummy_inputs_for_causal_lm(
                   lowercase_ ,		batch_size=lowercase_ ,		seq_length=lowercase_ ,		is_pair=lowercase_ ,		framework=lowercase_)
         else:
               lowercase_     			=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   lowercase_ ,		batch_size=lowercase_ ,		seq_length=lowercase_ ,		is_pair=lowercase_ ,		framework=lowercase_)

         return common_inputs


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Any)   ->       Union[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
               lowercase_     			=   super()._flatten_past_key_values_(lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_)
         else:
               lowercase_     			=   super(lowercase_ ,		self)._flatten_past_key_values_(
                   lowercase_ ,		lowercase_ ,		lowercase_ ,		lowercase_)


   def __UpperCAmelCase							( self					:						int ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						str)   ->       Optional[Any]:
         if direction not in ["inputs", "outputs"]:
               raise ValueError(F'direction must either be "inputs" or "outputs", but {direction} was given')

         lowercase_     			=   "past_key_values" if direction == "inputs" else "present"
         lowercase_     			=   self.num_layers

         lowercase_     			=   "past_encoder_sequence"
         lowercase_     			=   "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

         for i in range(lowercase_):
               lowercase_     			=   {0: "batch", 2: decoder_sequence}
               lowercase_     			=   {0: "batch", 2: decoder_sequence}
               lowercase_     			=   {0: "batch", 2: encoder_sequence}
               lowercase_     			=   {0: "batch", 2: encoder_sequence}








'''simple docstring'''
def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if right <= left:
        return
    snake_case_			:     Dict        =    left
    snake_case_			:     Tuple        =    right
    snake_case_			:     List[str]        =    sorting[left]
    while i <= b:
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    sorting[i], sorting[a]
            a += 1
            i += 1
        elif sorting[i] > pivot:
            snake_case_      ,			snake_case_			:     List[str]        =    sorting[i], sorting[b]
            b -= 1
        else:
            i += 1
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		a - 1							)
    quick_sort_apartition(__SCREAMING_SNAKE_CASE,		b + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if left < right:
        snake_case_			:     Dict        =    lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		pivot_index - 1							)
        quick_sort_lomuto_partition(__SCREAMING_SNAKE_CASE,		pivot_index + 1,		__SCREAMING_SNAKE_CASE							)

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     List[Any]        =    sorting[right]
    snake_case_			:     Optional[int]        =    left
    for i in range(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
        if sorting[i] < pivot:
            snake_case_      ,			snake_case_			:     Dict        =    sorting[i], sorting[store_index]
            store_index += 1
    snake_case_      ,			snake_case_			:     Optional[Any]        =    sorting[store_index], sorting[right]
    return store_index

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    if len(__SCREAMING_SNAKE_CASE							) <= 1:
        return sorting
    return (
        three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
        + [i for i in sorting if i == sorting[0]]
        + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)

  a_           =    input("Enter numbers separated by a comma:\n").strip()
  a_           =    [int(item) for item in user_input.split(",")]
  quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
  print(unsorted)




