

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 						lowercase     (					__A				):


		"""simple docstring"""




		def __call__(					self						,							__snake_case=None						,							__snake_case=None						,							**__snake_case):
			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(UpperCamelCase_						,							UpperCamelCase_) and table.get('query') is not None and table.get('table') is not None:
					_UpperCamelCase :		Tuple								=    [table]
				elif isinstance(UpperCamelCase_						,							UpperCamelCase_) and len(UpperCamelCase_) > 0:
					if not all(isinstance(UpperCamelCase_						,							UpperCamelCase_) for d in table):
						raise ValueError(
						    f'''Keyword argument `table` should be a list of dict, but is {(type(UpperCamelCase_) for d in table)}''')

					if table[0].get('query') is not None and table[0].get('table') is not None:
						_UpperCamelCase :		List[Any]								=    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(UpperCamelCase_						,							UpperCamelCase_) or isinstance(UpperCamelCase_						,							types.GeneratorType):
					return table
				else:
					raise ValueError(
					    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
					    f'''is {type(UpperCamelCase_)})''')
			else:
				_UpperCamelCase :		List[str]								=    [{'''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.')

					_UpperCamelCase :		Dict								=    pd.DataFrame(tqa_pipeline_input['table'])

			return tqa_pipeline_inputs







@add_end_docstrings(__A				)
class 						lowercase     (					__A				):


		"""simple docstring"""




		a__       							=	"table,query"


		def __init__(					self						,							__snake_case=TableQuestionAnsweringArgumentHandler()						,							*__snake_case						,							**__snake_case):
			super().__init__(*UpperCamelCase_						,							**UpperCamelCase_)
			_UpperCamelCase :		Optional[Any]								=    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()))

			_UpperCamelCase :		List[str]								=    bool(getattr(self.model.config						,							'aggregation_labels'						,							UpperCamelCase_)) and bool(
			    getattr(self.model.config						,							'num_aggregation_labels'						,							UpperCamelCase_))
			_UpperCamelCase :		Tuple								=    '''tapas''' if hasattr(self.model.config						,							'aggregation_labels') else None


		def 		A__       (					self						,							**__snake_case):
			return self.model(**UpperCamelCase_)


		def 		A__       (					self						,							**__snake_case):





			if self.framework == "pt":
				_UpperCamelCase :		Tuple								=    []
				_UpperCamelCase :		Dict								=    []
				_UpperCamelCase :		Dict								=    None
				_UpperCamelCase :		str								=    inputs['''input_ids'''].shape[0]

				_UpperCamelCase :		str								=    inputs['''input_ids'''].to(self.device)
				_UpperCamelCase :		str								=    inputs['''attention_mask'''].to(self.device)
				_UpperCamelCase :		Optional[Any]								=    inputs['''token_type_ids'''].to(self.device)
				_UpperCamelCase :		List[str]								=    None

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

						_UpperCamelCase :		Optional[Any]								=    token_type_ids[index]  # shape (seq_len, 7)
						for i in range(model_labels.shape[0]):
							_UpperCamelCase :		List[Any]								=    token_type_ids_example[:, 0].tolist()[i]
							_UpperCamelCase :		Optional[int]								=    token_type_ids_example[:, 1].tolist()[i] - 1
							_UpperCamelCase :		List[str]								=    token_type_ids_example[:, 2].tolist()[i] - 1

							if row_id >= 0 and col_id >= 0 and segment_id == 1:
								_UpperCamelCase :		Union[str, Any]								=    int(prev_answers[(col_id, row_id)])

						_UpperCamelCase :		Optional[Any]								=    torch.from_numpy(UpperCamelCase_).type(torch.long).to(self.device)

					_UpperCamelCase :		Dict								=    input_ids[index]
					_UpperCamelCase :		Tuple								=    attention_mask[index]  # shape (seq_len,)
					_UpperCamelCase :		Optional[int]								=    token_type_ids[index]  # shape (seq_len, 7)
					_UpperCamelCase :		int								=    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)						,							)
					_UpperCamelCase :		Dict								=    outputs.logits

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

					all_logits.append(UpperCamelCase_)

					_UpperCamelCase :		str								=    torch.distributions.Bernoulli(logits=UpperCamelCase_)
					_UpperCamelCase :		Dict								=    dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
					    dist_per_token.probs.device)

					_UpperCamelCase :		List[Any]								=    collections.defaultdict(UpperCamelCase_)
					for i, p in enumerate(probabilities.squeeze().tolist()):
						_UpperCamelCase :		str								=    token_type_ids_example[:, 0].tolist()[i]
						_UpperCamelCase :		str								=    token_type_ids_example[:, 1].tolist()[i] - 1
						_UpperCamelCase :		Optional[int]								=    token_type_ids_example[:, 2].tolist()[i] - 1
						if col >= 0 and row >= 0 and segment_id == 1:
							coords_to_probs[(col, row)].append(UpperCamelCase_)

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

				_UpperCamelCase :		List[Any]								=    torch.cat(tuple(UpperCamelCase_)						,							0)

				return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCamelCase_)						,							0))
			else:
				_UpperCamelCase :		Any								=    []
				_UpperCamelCase :		Dict								=    []
				_UpperCamelCase :		List[str]								=    None
				_UpperCamelCase :		Any								=    inputs['''input_ids'''].shape[0]

				_UpperCamelCase :		Optional[Any]								=    inputs['''input_ids''']
				_UpperCamelCase :		Dict								=    inputs['''attention_mask''']
				_UpperCamelCase :		str								=    inputs['''token_type_ids'''].numpy()
				_UpperCamelCase :		Optional[int]								=    None

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

						_UpperCamelCase :		List[str]								=    token_type_ids[index]  # shape (seq_len, 7)
						for i in range(model_labels.shape[0]):
							_UpperCamelCase :		Tuple								=    token_type_ids_example[:, 0].tolist()[i]
							_UpperCamelCase :		Optional[Any]								=    token_type_ids_example[:, 1].tolist()[i] - 1
							_UpperCamelCase :		str								=    token_type_ids_example[:, 2].tolist()[i] - 1

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

						_UpperCamelCase :		Any								=    model_labels

					_UpperCamelCase :		Union[str, Any]								=    input_ids[index]
					_UpperCamelCase :		Dict								=    attention_mask[index]  # shape (seq_len,)
					_UpperCamelCase :		List[str]								=    token_type_ids[index]  # shape (seq_len, 7)
					_UpperCamelCase :		Union[str, Any]								=    self.model(
					    input_ids=np.expand_dims(UpperCamelCase_						,							axis=0)						,							attention_mask=np.expand_dims(UpperCamelCase_						,							axis=0)						,							token_type_ids=np.expand_dims(UpperCamelCase_						,							axis=0)						,							)
					_UpperCamelCase :		Optional[Any]								=    outputs.logits

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

					all_logits.append(UpperCamelCase_)

					_UpperCamelCase :		List[Any]								=    tfp.distributions.Bernoulli(logits=UpperCamelCase_)
					_UpperCamelCase :		Tuple								=    dist_per_token.probs_parameter() * tf.cast(UpperCamelCase_						,							tf.floataa)

					_UpperCamelCase :		Tuple								=    collections.defaultdict(UpperCamelCase_)
					_UpperCamelCase :		List[str]								=    token_type_ids_example
					for i, p in enumerate(tf.squeeze(UpperCamelCase_).numpy().tolist()):
						_UpperCamelCase :		str								=    token_type_ids_example[:, 0].tolist()[i]
						_UpperCamelCase :		List[Any]								=    token_type_ids_example[:, 1].tolist()[i] - 1
						_UpperCamelCase :		Optional[Any]								=    token_type_ids_example[:, 2].tolist()[i] - 1
						if col >= 0 and row >= 0 and segment_id == 1:
							coords_to_probs[(col, row)].append(UpperCamelCase_)

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

				_UpperCamelCase :		Tuple								=    tf.concat(tuple(UpperCamelCase_)						,							0)

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


		def __call__(					self						,							*__snake_case						,							**__snake_case):
			_UpperCamelCase :		Tuple								=    self._args_parser(*UpperCamelCase_						,							**UpperCamelCase_)

			_UpperCamelCase :		int								=    super().__call__(UpperCamelCase_						,							**UpperCamelCase_)
			if len(UpperCamelCase_) == 1:
				return results[0]
			return results


		def 		A__       (					self						,							__snake_case=None						,							__snake_case=None						,							__snake_case=None						,							**__snake_case):
			_UpperCamelCase :		Tuple								=    {}
			if padding is not None:
				_UpperCamelCase :		Any								=    padding
			if truncation is not None:
				_UpperCamelCase :		Tuple								=    truncation

			_UpperCamelCase :		Tuple								=    {}
			if sequential is not None:
				_UpperCamelCase :		Optional[int]								=    sequential
			return preprocess_params, forward_params, {}


		def 		A__       (					self						,							__snake_case						,							__snake_case=None						,							__snake_case=True						,							__snake_case=None):





			if truncation is None:
				if self.type == "tapas":
					_UpperCamelCase :		Union[str, Any]								=    '''drop_rows_to_fit'''
				else:
					_UpperCamelCase :		str								=    '''do_not_truncate'''

			_UpperCamelCase :		Any								=    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')
			_UpperCamelCase :		Any								=    self.tokenizer(UpperCamelCase_						,							UpperCamelCase_						,							return_tensors=self.framework						,							truncation=UpperCamelCase_						,							padding=UpperCamelCase_)
			_UpperCamelCase :		str								=    table
			return inputs


		def 		A__       (					self						,							__snake_case						,							__snake_case=False):
			_UpperCamelCase :		Dict								=    model_inputs.pop('table')

			if self.type == "tapas":
				if sequential:
					_UpperCamelCase :		Union[str, Any]								=    self.sequential_inference(**UpperCamelCase_)
				else:
					_UpperCamelCase :		Dict								=    self.batch_inference(**UpperCamelCase_)
			else:
				_UpperCamelCase :		int								=    self.model.generate(**UpperCamelCase_)
			_UpperCamelCase :		List[Any]								=    {'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
			return model_outputs






		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		str								=    model_outputs['''model_inputs''']
			_UpperCamelCase :		List[Any]								=    model_outputs['''table''']
			_UpperCamelCase :		int								=    model_outputs['''outputs''']
			if self.type == "tapas":
				if self.aggregate:
					_UpperCamelCase :		Dict								=    outputs[:2]
					_UpperCamelCase :		Optional[int]								=    self.tokenizer.convert_logits_to_predictions(UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_)
					_UpperCamelCase :		Union[str, Any]								=    predictions
					_UpperCamelCase :		Tuple								=    {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCamelCase_)}

					_UpperCamelCase :		Dict								=    self.model.config.no_aggregation_label_index
					_UpperCamelCase :		Tuple								=    {
					    i: aggregators[i] + ''' > ''' for i, pred in enumerate(UpperCamelCase_) if pred != no_agg_label_index
					}
				else:
					_UpperCamelCase :		Optional[int]								=    outputs[0]
					_UpperCamelCase :		Optional[Any]								=    self.tokenizer.convert_logits_to_predictions(UpperCamelCase_						,							UpperCamelCase_)
					_UpperCamelCase :		str								=    predictions[0]
					_UpperCamelCase :		Optional[int]								=    {}
					_UpperCamelCase :		Optional[Any]								=    {}
				_UpperCamelCase :		Optional[int]								=    []
				for index, coordinates in enumerate(UpperCamelCase_):
					_UpperCamelCase :		List[str]								=    [table.iat[coordinate] for coordinate in coordinates]
					_UpperCamelCase :		Union[str, Any]								=    aggregators.get(UpperCamelCase_						,							'')
					_UpperCamelCase :		str								=    aggregators_prefix.get(UpperCamelCase_						,							'')
					_UpperCamelCase :		List[Any]								=    {
					    '''answer''': aggregator_prefix + ''', '''.join(UpperCamelCase_),
					    '''coordinates''': coordinates,
					    '''cells''': [table.iat[coordinate] for coordinate in coordinates],
					}
					if aggregator:
						_UpperCamelCase :		Union[str, Any]								=    aggregator

					answers.append(UpperCamelCase_)
				if len(UpperCamelCase_) == 0:
					raise PipelineException('Empty answer')
			else:
				_UpperCamelCase :		Optional[int]								=    [{'''answer''': answer} for answer in self.tokenizer.batch_decode(UpperCamelCase_						,							skip_special_tokens=UpperCamelCase_)]

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









'''simple docstring'''






def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     List[str]  )		->Dict:






							'''simple docstring'''
							_lowercase							:		Optional[Any]				=   [7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
							for gap in gaps:
														for i in range(snake_case_ ,       len(snake_case_  )  ):
																					_lowercase							:		int				=   collection[i]
																					_lowercase							:		Any				=   i
																					while j >= gap and collection[j - gap] > insert_value:
																												_lowercase							:		Optional[int]				=   collection[j - gap]
																												j -= gap
																					if j != i:
																												_lowercase							:		str				=   insert_value
							return collection


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCamelCase__	  =		input('Enter numbers separated by a comma:\n').strip()
						lowerCamelCase__	  =		[int(item) for item in user_input.split(',')]
						print(shell_sort(unsorted))



