

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__							=			logging.get_logger(__name__)

lowerCAmelCase__							=			{
    "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     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    '''blenderbot'''
 UpperCAmelCase				     =    ['''past_key_values''']
 UpperCAmelCase				     =    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Tuple=8_0_0_8      ,    lowerCAmelCase__		:			int=1_2_8      ,    lowerCAmelCase__		:			Optional[int]=2      ,    lowerCAmelCase__		:			Tuple=1_0_2_4_0      ,    lowerCAmelCase__		:			List[Any]=3_2      ,    lowerCAmelCase__		:			Optional[Any]=2_4      ,    lowerCAmelCase__		:			Optional[int]=1_0_2_4_0      ,    lowerCAmelCase__		:			Optional[int]=3_2      ,    lowerCAmelCase__		:			Dict=0.0      ,    lowerCAmelCase__		:			Dict=0.0      ,    lowerCAmelCase__		:			Any=True      ,    lowerCAmelCase__		:			str=True      ,    lowerCAmelCase__		:			Tuple="gelu"      ,    lowerCAmelCase__		:			Union[str, Any]=2_5_6_0      ,    lowerCAmelCase__		:			Dict=0.1      ,    lowerCAmelCase__		:			Union[str, Any]=0.0      ,    lowerCAmelCase__		:			Tuple=0.0      ,    lowerCAmelCase__		:			Optional[int]=0.02      ,    lowerCAmelCase__		:			List[Any]=1      ,    lowerCAmelCase__		:			List[Any]=False      ,    lowerCAmelCase__		:			List[str]=0      ,    lowerCAmelCase__		:			List[Any]=1      ,    lowerCAmelCase__		:			int=2      ,    lowerCAmelCase__		:			Dict=3      ,    lowerCAmelCase__		:			int=2      ,    **lowerCAmelCase__		:			Dict      ,    )     ->   Tuple:
   UpperCAmelCase          =	vocab_size
   UpperCAmelCase          =	max_position_embeddings
   UpperCAmelCase          =	d_model
   UpperCAmelCase          =	encoder_ffn_dim
   UpperCAmelCase          =	encoder_layers
   UpperCAmelCase          =	encoder_attention_heads
   UpperCAmelCase          =	decoder_ffn_dim
   UpperCAmelCase          =	decoder_layers
   UpperCAmelCase          =	decoder_attention_heads
   UpperCAmelCase          =	dropout
   UpperCAmelCase          =	attention_dropout
   UpperCAmelCase          =	activation_dropout
   UpperCAmelCase          =	activation_function
   UpperCAmelCase          =	init_std
   UpperCAmelCase          =	encoder_layerdrop
   UpperCAmelCase          =	decoder_layerdrop
   UpperCAmelCase          =	use_cache
   UpperCAmelCase          =	encoder_layers
   UpperCAmelCase          =	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     __magic_name__     (							_snake_case     ):


 @property
 def _UpperCamelCase						(    self		:			List[Any]     )     ->   str:
   if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase          =	OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
         ]     )
     if self.use_past:
       UpperCAmelCase          =	{0: "batch"}
       UpperCAmelCase          =	{0: "batch", 1: "past_decoder_sequence + sequence"}
     else:
       UpperCAmelCase          =	{0: "batch", 1: "decoder_sequence"}
       UpperCAmelCase          =	{0: "batch", 1: "decoder_sequence"}
     if self.use_past:
       self.fill_with_past_key_values_(lowercase__      ,    direction="inputs"     )
   elif self.task == "causal-lm":
     UpperCAmelCase          =	OrderedDict(
         [
             ("input_ids", {0: "batch", 1: "encoder_sequence"}),
             ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
         ]     )
     if self.use_past:
       UpperCAmelCase   ,						UpperCAmelCase          =	self.num_layers
       for i in range(lowercase__     ):
         UpperCAmelCase          =	{0: "batch", 2: "past_sequence + sequence"}
         UpperCAmelCase          =	{0: "batch", 2: "past_sequence + sequence"}
   else:
     UpperCAmelCase          =	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		:			str     )     ->   Tuple:
   if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase          =	super().outputs
   else:
     UpperCAmelCase          =	super(lowercase__      ,    self     ).outputs
     if self.use_past:
       UpperCAmelCase   ,						UpperCAmelCase          =	self.num_layers
       for i in range(lowercase__     ):
         UpperCAmelCase          =	{0: "batch", 2: "past_sequence + sequence"}
         UpperCAmelCase          =	{0: "batch", 2: "past_sequence + sequence"}
   return common_outputs


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			PreTrainedTokenizer      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			Optional[TensorType] = None      ,    )     ->   Tuple:
   UpperCAmelCase          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__     )
   # Generate decoder inputs
   UpperCAmelCase          =	seq_length if not self.use_past else 1
   UpperCAmelCase          =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
       lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__     )
   UpperCAmelCase          =	{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
   UpperCAmelCase          =	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
     UpperCAmelCase   ,						UpperCAmelCase          =	common_inputs["input_ids"].shape
     UpperCAmelCase          =	common_inputs["decoder_input_ids"].shape[1]
     UpperCAmelCase   ,						UpperCAmelCase          =	self.num_attention_heads
     UpperCAmelCase          =	(
         batch,
         num_encoder_attention_heads,
         encoder_seq_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCAmelCase          =	decoder_seq_length
     UpperCAmelCase          =	(
         batch,
         num_decoder_attention_heads,
         decoder_past_length,
         self._config.hidden_size // num_decoder_attention_heads,
     )
     UpperCAmelCase          =	torch.cat(
         [common_inputs["decoder_attention_mask"], torch.ones(lowercase__      ,    lowercase__     )]      ,    dim=1     )
     UpperCAmelCase          =	[]
     UpperCAmelCase   ,						UpperCAmelCase          =	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		:			Tuple      ,    lowerCAmelCase__		:			PreTrainedTokenizer      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			Optional[TensorType] = None      ,    )     ->   List[str]:
   UpperCAmelCase          =	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
     UpperCAmelCase   ,						UpperCAmelCase          =	common_inputs["input_ids"].shape
     UpperCAmelCase          =	seqlen
     UpperCAmelCase   ,						UpperCAmelCase          =	self.num_layers
     UpperCAmelCase   ,						UpperCAmelCase          =	self.num_attention_heads
     UpperCAmelCase          =	(
         batch,
         num_encoder_attention_heads,
         past_key_values_length,
         self._config.hidden_size // num_encoder_attention_heads,
     )
     UpperCAmelCase          =	common_inputs["attention_mask"].dtype
     UpperCAmelCase          =	torch.cat(
         [common_inputs["attention_mask"], torch.ones(lowercase__      ,    lowercase__      ,    dtype=lowercase__     )]      ,    dim=1     )
     UpperCAmelCase          =	[
         (torch.zeros(lowercase__     ), torch.zeros(lowercase__     )) for _ in range(lowercase__     )
     ]
   return common_inputs


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			PreTrainedTokenizer      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			Optional[TensorType] = None      ,    )     ->   str:
   # 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
   UpperCAmelCase          =	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
   UpperCAmelCase          =	tokenizer.num_special_tokens_to_add(lowercase__     )
   UpperCAmelCase          =	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
   UpperCAmelCase          =	[" ".join([tokenizer.unk_token]     ) * seq_length] * batch_size
   UpperCAmelCase          =	dict(tokenizer(lowercase__      ,    return_tensors=lowercase__     )     )
   return common_inputs


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			PreTrainedTokenizer      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			int = -1      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			Optional[TensorType] = None      ,    )     ->   Union[str, Any]:
   if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase          =	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":
     UpperCAmelCase          =	self._generate_dummy_inputs_for_causal_lm(
         lowercase__      ,    batch_size=lowercase__      ,    seq_length=lowercase__      ,    is_pair=lowercase__      ,    framework=lowercase__     )
   else:
     UpperCAmelCase          =	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		:			Optional[Any]      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			List[str]      ,    lowerCAmelCase__		:			Optional[Any]      ,    lowerCAmelCase__		:			List[Any]     )     ->   Any:
   if self.task in ["default", "seq2seq-lm"]:
     UpperCAmelCase          =	super()._flatten_past_key_values_(lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__     )
   else:
     UpperCAmelCase          =	super(lowercase__      ,    self     )._flatten_past_key_values_(
         lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Mapping[str, Mapping[int, str]]      ,    lowerCAmelCase__		:			str     )     ->   Dict:
   if direction not in ["inputs", "outputs"]:
     raise ValueError(f"direction must either be \"inputs\" or \"outputs\", but {direction} was given"     )

   UpperCAmelCase          =	"past_key_values" if direction == "inputs" else "present"
   UpperCAmelCase   ,						UpperCAmelCase          =	self.num_layers

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

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








def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if right <= left:
										return
					lowercase						   =       lowercase						   =       left
					lowercase						   =       right
					lowercase						   =       sorting[left]
					while i <= b:
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[a]
															a += 1
															i += 1
										elif sorting[i] > pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[b]
															b -= 1
										else:
															i += 1
					quick_sort_apartition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			a - 1				)
					quick_sort_apartition(_UpperCAmelCase       ,			b + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					if left < right:
										lowercase						   =       lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			_UpperCAmelCase       ,			pivot_index - 1				)
										quick_sort_lomuto_partition(_UpperCAmelCase       ,			pivot_index + 1       ,			_UpperCAmelCase				)





def 		__snake_case						(    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase				):
					"""simple docstring"""






					lowercase						   =       sorting[right]
					lowercase						   =       left
					for i in range(_UpperCAmelCase       ,			_UpperCAmelCase				):
										if sorting[i] < pivot:
															lowercase   ,      lowercase						   =       sorting[i], sorting[store_index]
															store_index += 1
					lowercase   ,      lowercase						   =       sorting[store_index], sorting[right]
					return store_index





def 		__snake_case						(    _UpperCAmelCase				):
					"""simple docstring"""






					if len(_UpperCAmelCase				) <= 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)

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






