
'''simple docstring'''



import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import (
				    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				)

if is_tf_available() and is_tensorflow_probability_available():
				import tensorflow as tf
				import tensorflow_probability as tfp

				from ..models.auto.modeling_tf_auto import (
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				)





class      A							(    __UpperCAmelCase			):



			def __call__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       **lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									requires_backends(self    ,       """pandas"""    )
									import pandas as pd

									if table is None:
															raise ValueError("""Keyword argument `table` cannot be None."""    )
									elif query is None:
															if isinstance(lowerCamelCase__    ,       lowerCamelCase__    ) and table.get("""query"""    ) is not None and table.get("""table"""    ) is not None:
																					lowercase__								=    [table]
															elif isinstance(lowerCamelCase__    ,       lowerCamelCase__    ) and len(lowerCamelCase__    ) > 0:
																					if not all(isinstance(lowerCamelCase__    ,       lowerCamelCase__    ) for d in table    ):
																											raise ValueError(
																											    F'''Keyword argument `table` should be a list of dict, but is {(type(lowerCamelCase__    ) for d in table)}'''    )

																					if table[0].get("""query"""    ) is not None and table[0].get("""table"""    ) is not None:
																											lowercase__								=    table
																					else:
																											raise ValueError(
																											    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
																											    F''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''    )
															elif Dataset is not None and isinstance(lowerCamelCase__    ,       lowerCamelCase__    ) or isinstance(lowerCamelCase__    ,       types.GeneratorType    ):
																					return table
															else:
																					raise ValueError(
																					    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																					    F'''is {type(lowerCamelCase__    )})'''    )
									else:
															lowercase__								=    [{"""table""": table, """query""": query}]

									for tqa_pipeline_input in tqa_pipeline_inputs:
															if not isinstance(tqa_pipeline_input["""table"""]    ,       pd.DataFrame    ):
																					if tqa_pipeline_input["table"] is None:
																											raise ValueError("""Table cannot be None."""    )

																					lowercase__								=    pd.DataFrame(tqa_pipeline_input["""table"""]    )

									return tqa_pipeline_inputs





@add_end_docstrings(__UpperCAmelCase			)
class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[str]    						=					"""table,query"""



			def __init__(   self    ,       lowerCamelCase__=TableQuestionAnsweringArgumentHandler()    ,       *lowerCamelCase__    ,       **lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									super().__init__(*lowerCamelCase__    ,       **lowerCamelCase__    )
									lowercase__								=    args_parser

									self.check_model_type(
									    dict(
									        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
									        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )
									    if self.framework == """tf"""
									    else dict(
									        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()    )    )

									lowercase__								=    bool(getattr(self.model.config    ,       """aggregation_labels"""    ,       lowerCamelCase__    )    ) and bool(
									    getattr(self.model.config    ,       """num_aggregation_labels"""    ,       lowerCamelCase__    )    )
									lowercase__								=    """tapas""" if hasattr(self.model.config    ,       """aggregation_labels"""    ) else None



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Optional[Any]:




									'''simple docstring'''



									return self.model(**lowerCamelCase__    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									if self.framework == "pt":
															lowercase__								=    []
															lowercase__								=    []
															lowercase__								=    None
															lowercase__								=    inputs["""input_ids"""].shape[0]

															lowercase__								=    inputs["""input_ids"""].to(self.device    )
															lowercase__								=    inputs["""attention_mask"""].to(self.device    )
															lowercase__								=    inputs["""token_type_ids"""].to(self.device    )
															lowercase__								=    None

															for index in range(lowerCamelCase__    ):
																					# If sequences have already been processed, the token type IDs will be created according to the previous
																					# answer.
																					if prev_answers is not None:
																											lowercase__								=    token_type_ids_example[:, 3]  # shape (seq_len,)
																											lowercase__								=    np.zeros_like(prev_labels_example.cpu().numpy()    )  # shape (seq_len,)

																											lowercase__								=    token_type_ids[index]  # shape (seq_len, 7)
																											for i in range(model_labels.shape[0]    ):
																																	lowercase__								=    token_type_ids_example[:, 0].tolist()[i]
																																	lowercase__								=    token_type_ids_example[:, 1].tolist()[i] - 1
																																	lowercase__								=    token_type_ids_example[:, 2].tolist()[i] - 1

																																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																							lowercase__								=    int(prev_answers[(col_id, row_id)]    )

																											lowercase__								=    torch.from_numpy(lowerCamelCase__    ).type(torch.long    ).to(self.device    )

																					lowercase__								=    input_ids[index]
																					lowercase__								=    attention_mask[index]  # shape (seq_len,)
																					lowercase__								=    token_type_ids[index]  # shape (seq_len, 7)
																					lowercase__								=    self.model(
																					    input_ids=input_ids_example.unsqueeze(0    )    ,       attention_mask=attention_mask_example.unsqueeze(0    )    ,       token_type_ids=token_type_ids_example.unsqueeze(0    )    ,       )
																					lowercase__								=    outputs.logits

																					if self.aggregate:
																											all_aggregations.append(outputs.logits_aggregation    )

																					all_logits.append(lowerCamelCase__    )

																					lowercase__								=    torch.distributions.Bernoulli(logits=lowerCamelCase__    )
																					lowercase__								=    dist_per_token.probs * attention_mask_example.type(torch.floataa    ).to(
																					    dist_per_token.probs.device    )

																					lowercase__								=    collections.defaultdict(lowerCamelCase__    )
																					for i, p in enumerate(probabilities.squeeze().tolist()    ):
																											lowercase__								=    token_type_ids_example[:, 0].tolist()[i]
																											lowercase__								=    token_type_ids_example[:, 1].tolist()[i] - 1
																											lowercase__								=    token_type_ids_example[:, 2].tolist()[i] - 1
																											if col >= 0 and row >= 0 and segment_id == 1:
																																	coords_to_probs[(col, row)].append(lowerCamelCase__    )

																					lowercase__								=    {key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}

															lowercase__								=    torch.cat(tuple(lowerCamelCase__    )    ,       0    )

															return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowerCamelCase__    )    ,       0    ))
									else:
															lowercase__								=    []
															lowercase__								=    []
															lowercase__								=    None
															lowercase__								=    inputs["""input_ids"""].shape[0]

															lowercase__								=    inputs["""input_ids"""]
															lowercase__								=    inputs["""attention_mask"""]
															lowercase__								=    inputs["""token_type_ids"""].numpy()
															lowercase__								=    None

															for index in range(lowerCamelCase__    ):
																					# If sequences have already been processed, the token type IDs will be created according to the previous
																					# answer.
																					if prev_answers is not None:
																											lowercase__								=    token_type_ids_example[:, 3]  # shape (seq_len,)
																											lowercase__								=    np.zeros_like(lowerCamelCase__    ,       dtype=np.intaa    )  # shape (seq_len,)

																											lowercase__								=    token_type_ids[index]  # shape (seq_len, 7)
																											for i in range(model_labels.shape[0]    ):
																																	lowercase__								=    token_type_ids_example[:, 0].tolist()[i]
																																	lowercase__								=    token_type_ids_example[:, 1].tolist()[i] - 1
																																	lowercase__								=    token_type_ids_example[:, 2].tolist()[i] - 1

																																	if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																							lowercase__								=    int(prev_answers[(col_id, row_id)]    )

																											lowercase__								=    model_labels

																					lowercase__								=    input_ids[index]
																					lowercase__								=    attention_mask[index]  # shape (seq_len,)
																					lowercase__								=    token_type_ids[index]  # shape (seq_len, 7)
																					lowercase__								=    self.model(
																					    input_ids=np.expand_dims(lowerCamelCase__    ,       axis=0    )    ,       attention_mask=np.expand_dims(lowerCamelCase__    ,       axis=0    )    ,       token_type_ids=np.expand_dims(lowerCamelCase__    ,       axis=0    )    ,       )
																					lowercase__								=    outputs.logits

																					if self.aggregate:
																											all_aggregations.append(outputs.logits_aggregation    )

																					all_logits.append(lowerCamelCase__    )

																					lowercase__								=    tfp.distributions.Bernoulli(logits=lowerCamelCase__    )
																					lowercase__								=    dist_per_token.probs_parameter() * tf.cast(lowerCamelCase__    ,       tf.floataa    )

																					lowercase__								=    collections.defaultdict(lowerCamelCase__    )
																					lowercase__								=    token_type_ids_example
																					for i, p in enumerate(tf.squeeze(lowerCamelCase__    ).numpy().tolist()    ):
																											lowercase__								=    token_type_ids_example[:, 0].tolist()[i]
																											lowercase__								=    token_type_ids_example[:, 1].tolist()[i] - 1
																											lowercase__								=    token_type_ids_example[:, 2].tolist()[i] - 1
																											if col >= 0 and row >= 0 and segment_id == 1:
																																	coords_to_probs[(col, row)].append(lowerCamelCase__    )

																					lowercase__								=    {key: np.array(coords_to_probs[key]    ).mean() > 0.5 for key in coords_to_probs}

															lowercase__								=    tf.concat(tuple(lowerCamelCase__    )    ,       0    )

															return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowerCamelCase__    )    ,       0    ))



			def __call__(   self    ,       *lowerCamelCase__    ,       **lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    self._args_parser(*lowerCamelCase__    ,       **lowerCamelCase__    )

									lowercase__								=    super().__call__(lowerCamelCase__    ,       **lowerCamelCase__    )
									if len(lowerCamelCase__    ) == 1:
															return results[0]
									return results



			def A__	(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       **lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    {}
									if padding is not None:
															lowercase__								=    padding
									if truncation is not None:
															lowercase__								=    truncation

									lowercase__								=    {}
									if sequential is not None:
															lowercase__								=    sequential
									return preprocess_params, forward_params, {}



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=None    ,       lowerCamelCase__=True    ,       lowerCamelCase__=None    )     ->						Any:




									'''simple docstring'''



									if truncation is None:
															if self.type == "tapas":
																					lowercase__								=    """drop_rows_to_fit"""
															else:
																					lowercase__								=    """do_not_truncate"""

									lowercase__	,  lowercase__								=    pipeline_input["""table"""], pipeline_input["""query"""]
									if table.empty:
															raise ValueError("""table is empty"""    )
									if query is None or query == "":
															raise ValueError("""query is empty"""    )
									lowercase__								=    self.tokenizer(lowerCamelCase__    ,       lowerCamelCase__    ,       return_tensors=self.framework    ,       truncation=lowerCamelCase__    ,       padding=lowerCamelCase__    )
									lowercase__								=    table
									return inputs



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=False    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    model_inputs.pop("""table"""    )

									if self.type == "tapas":
															if sequential:
																					lowercase__								=    self.sequential_inference(**lowerCamelCase__    )
															else:
																					lowercase__								=    self.batch_inference(**lowerCamelCase__    )
									else:
															lowercase__								=    self.model.generate(**lowerCamelCase__    )
									lowercase__								=    {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
									return model_outputs



			def A__	(   self    ,       lowerCamelCase__    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    model_outputs["""model_inputs"""]
									lowercase__								=    model_outputs["""table"""]
									lowercase__								=    model_outputs["""outputs"""]
									if self.type == "tapas":
															if self.aggregate:
																					lowercase__	,  lowercase__								=    outputs[:2]
																					lowercase__								=    self.tokenizer.convert_logits_to_predictions(lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    )
																					lowercase__	,  lowercase__								=    predictions
																					lowercase__								=    {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowerCamelCase__    )}

																					lowercase__								=    self.model.config.no_aggregation_label_index
																					lowercase__								=    {
																					    i: aggregators[i] + """ > """ for i, pred in enumerate(lowerCamelCase__    ) if pred != no_agg_label_index
																					}
															else:
																					lowercase__								=    outputs[0]
																					lowercase__								=    self.tokenizer.convert_logits_to_predictions(lowerCamelCase__    ,       lowerCamelCase__    )
																					lowercase__								=    predictions[0]
																					lowercase__								=    {}
																					lowercase__								=    {}
															lowercase__								=    []
															for index, coordinates in enumerate(lowerCamelCase__    ):
																					lowercase__								=    [table.iat[coordinate] for coordinate in coordinates]
																					lowercase__								=    aggregators.get(lowerCamelCase__    ,       """"""    )
																					lowercase__								=    aggregators_prefix.get(lowerCamelCase__    ,       """"""    )
																					lowercase__								=    {
																					    """answer""": aggregator_prefix + """, """.join(lowerCamelCase__    ),
																					    """coordinates""": coordinates,
																					    """cells""": [table.iat[coordinate] for coordinate in coordinates],
																					}
																					if aggregator:
																											lowercase__								=    aggregator

																					answers.append(lowerCamelCase__    )
															if len(lowerCamelCase__    ) == 0:
																					raise PipelineException("""Empty answer"""    )
									else:
															lowercase__								=    [{"""answer""": answer} for answer in self.tokenizer.batch_decode(lowerCamelCase__    ,       skip_special_tokens=lowerCamelCase__    )]

									return answers if len(lowerCamelCase__    ) > 1 else answers[0]








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						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(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												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 	_A						(							lowercase__		):
						if len(lowercase__		) <= 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)






