



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


__magic_name__       =     logging.get_logger(__name__)

__magic_name__       =     {
    '''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 	a__       (       _snake_case       ):


					"""simple docstring"""



					A__  :				Optional[int]					 =				'''blenderbot'''
					A__  :				int					 =				['''past_key_values''']
					A__  :				Dict					 =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

					def __init__(		self     :Optional[int]      ,  lowercase__     :Tuple=8008      ,  lowercase__     :int=128      ,  lowercase__     :Optional[int]=2      ,  lowercase__     :Tuple=1_0240      ,  lowercase__     :List[Any]=32      ,  lowercase__     :Optional[Any]=24      ,  lowercase__     :Optional[int]=1_0240      ,  lowercase__     :Optional[int]=32      ,  lowercase__     :Dict=0.0      ,  lowercase__     :Dict=0.0      ,  lowercase__     :Any=True      ,  lowercase__     :str=True      ,  lowercase__     :Tuple="gelu"      ,  lowercase__     :Union[str, Any]=2560      ,  lowercase__     :Dict=0.1      ,  lowercase__     :Union[str, Any]=0.0      ,  lowercase__     :Tuple=0.0      ,  lowercase__     :Optional[int]=0.02      ,  lowercase__     :List[Any]=1      ,  lowercase__     :List[Any]=False      ,  lowercase__     :List[str]=0      ,  lowercase__     :List[Any]=1      ,  lowercase__     :int=2      ,  lowercase__     :Dict=3      ,  lowercase__     :int=2      ,  **lowercase__     :Dict      ,  ):
										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 	a__       (       _snake_case       ):


					"""simple docstring"""



					@property
					def 				__UpperCAmelCase		(		self     :List[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   ,      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     :str    ):
										if self.task in ["default", "seq2seq-lm"]:
															lowercase						   =       super().outputs
										else:
															lowercase						   =       super(lowercase__      ,  self    ).outputs
															if self.use_past:
																				lowercase   ,      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     :Union[str, Any]      ,  lowercase__     :PreTrainedTokenizer      ,  lowercase__     :int = -1      ,  lowercase__     :int = -1      ,  lowercase__     :bool = False      ,  lowercase__     :Optional[TensorType] = None      ,  ):
										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   ,      lowercase						   =       common_inputs['input_ids'].shape
															lowercase						   =       common_inputs['decoder_input_ids'].shape[1]
															lowercase   ,      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   ,      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     :Tuple      ,  lowercase__     :PreTrainedTokenizer      ,  lowercase__     :int = -1      ,  lowercase__     :int = -1      ,  lowercase__     :bool = False      ,  lowercase__     :Optional[TensorType] = None      ,  ):
										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   ,      lowercase						   =       common_inputs['input_ids'].shape
															lowercase						   =       seqlen
															lowercase   ,      lowercase						   =       self.num_layers
															lowercase   ,      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     :Union[str, Any]      ,  lowercase__     :PreTrainedTokenizer      ,  lowercase__     :int = -1      ,  lowercase__     :int = -1      ,  lowercase__     :bool = False      ,  lowercase__     :Optional[TensorType] = None      ,  ):
										# 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[Any]      ,  lowercase__     :PreTrainedTokenizer      ,  lowercase__     :int = -1      ,  lowercase__     :int = -1      ,  lowercase__     :bool = False      ,  lowercase__     :Optional[TensorType] = None      ,  ):
										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     :Optional[Any]      ,  lowercase__     :List[str]      ,  lowercase__     :List[str]      ,  lowercase__     :Optional[Any]      ,  lowercase__     :List[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     :Any      ,  lowercase__     :Mapping[str, Mapping[int, str]]      ,  lowercase__     :str    ):
										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   ,      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}











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)






