



"""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 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __call__(			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Any=None    ,					**_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  Tuple:





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

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

													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    pd.DataFrame(tqa_pipeline_input['table']						)

					return tqa_pipeline_inputs




@add_end_docstrings(_lowerCamelCase   )
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Tuple     	=   '''table,query'''





	def __init__(			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple=TableQuestionAnsweringArgumentHandler()    ,					*_SCREAMING_SNAKE_CASE							:					Optional[int]    ,					**_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  List[str]:





					"""simple docstring"""


					super().__init__(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    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()						)						)

					SCREAMING_SNAKE_CASE	:       int 							=    bool(getattr(self.model.config    ,					'aggregation_labels'    ,					_SCREAMING_SNAKE_CASE						)						) and bool(
					    getattr(self.model.config    ,					'num_aggregation_labels'    ,					_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'tapas' if hasattr(self.model.config    ,					'aggregation_labels'						) else None





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					**_SCREAMING_SNAKE_CASE							:					Dict						)	->  List[str]:





					"""simple docstring"""


					return self.model(**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  Dict:





					"""simple docstring"""


					if self.framework == "pt":
									SCREAMING_SNAKE_CASE	:       Tuple 							=    []
									SCREAMING_SNAKE_CASE	:       List[str] 							=    []
									SCREAMING_SNAKE_CASE	:       Tuple 							=    None
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    inputs['input_ids'].shape[0]

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

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

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

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

																	SCREAMING_SNAKE_CASE	:       int 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).type(torch.long						).to(self.device						)

													SCREAMING_SNAKE_CASE	:       List[Any] 							=    input_ids[index]
													SCREAMING_SNAKE_CASE	:       Dict 							=    attention_mask[index]  # shape (seq_len,)
													SCREAMING_SNAKE_CASE	:       Any 							=    token_type_ids[index]  # shape (seq_len, 7)
													SCREAMING_SNAKE_CASE	:       Any 							=    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						)    ,					)
													SCREAMING_SNAKE_CASE	:       Any 							=    outputs.logits

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

													all_logits.append(_SCREAMING_SNAKE_CASE						)

													SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       str 							=    dist_per_token.probs * attention_mask_example.type(torch.floataa						).to(
													    dist_per_token.probs.device						)

													SCREAMING_SNAKE_CASE	:       List[str] 							=    collections.defaultdict(_SCREAMING_SNAKE_CASE						)
													for i, p in enumerate(probabilities.squeeze().tolist()						):
																	SCREAMING_SNAKE_CASE	:       Tuple 							=    token_type_ids_example[:, 0].tolist()[i]
																	SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    token_type_ids_example[:, 1].tolist()[i] - 1
																	SCREAMING_SNAKE_CASE	:       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(_SCREAMING_SNAKE_CASE						)

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

									SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    torch.cat(tuple(_SCREAMING_SNAKE_CASE						)    ,					0						)

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

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

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

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

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

																	SCREAMING_SNAKE_CASE	:       Any 							=    model_labels

													SCREAMING_SNAKE_CASE	:       Any 							=    input_ids[index]
													SCREAMING_SNAKE_CASE	:       str 							=    attention_mask[index]  # shape (seq_len,)
													SCREAMING_SNAKE_CASE	:       List[Any] 							=    token_type_ids[index]  # shape (seq_len, 7)
													SCREAMING_SNAKE_CASE	:       str 							=    self.model(
													    input_ids=np.expand_dims(_SCREAMING_SNAKE_CASE    ,					axis=0						)    ,					attention_mask=np.expand_dims(_SCREAMING_SNAKE_CASE    ,					axis=0						)    ,					token_type_ids=np.expand_dims(_SCREAMING_SNAKE_CASE    ,					axis=0						)    ,					)
													SCREAMING_SNAKE_CASE	:       int 							=    outputs.logits

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

													all_logits.append(_SCREAMING_SNAKE_CASE						)

													SCREAMING_SNAKE_CASE	:       List[Any] 							=    tfp.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    dist_per_token.probs_parameter() * tf.cast(_SCREAMING_SNAKE_CASE    ,					tf.floataa						)

													SCREAMING_SNAKE_CASE	:       int 							=    collections.defaultdict(_SCREAMING_SNAKE_CASE						)
													SCREAMING_SNAKE_CASE	:       Tuple 							=    token_type_ids_example
													for i, p in enumerate(tf.squeeze(_SCREAMING_SNAKE_CASE						).numpy().tolist()						):
																	SCREAMING_SNAKE_CASE	:       Optional[int] 							=    token_type_ids_example[:, 0].tolist()[i]
																	SCREAMING_SNAKE_CASE	:       int 							=    token_type_ids_example[:, 1].tolist()[i] - 1
																	SCREAMING_SNAKE_CASE	:       Union[str, 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(_SCREAMING_SNAKE_CASE						)

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

									SCREAMING_SNAKE_CASE	:       str 							=    tf.concat(tuple(_SCREAMING_SNAKE_CASE						)    ,					0						)

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





	def __call__(			self							:					List[str]    ,					*_SCREAMING_SNAKE_CASE							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self._args_parser(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Tuple 							=    super().__call__(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)
					if len(_SCREAMING_SNAKE_CASE						) == 1:
									return results[0]
					return results





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Any=None    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    {}
					if padding is not None:
									SCREAMING_SNAKE_CASE	:       Any 							=    padding
					if truncation is not None:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    truncation

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





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Dict=None    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None						)	->  Union[str, Any]:





					"""simple docstring"""


					if truncation is None:
									if self.type == "tapas":
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    'drop_rows_to_fit'
									else:
													SCREAMING_SNAKE_CASE	:       Dict 							=    'do_not_truncate'

					SCREAMING_SNAKE_CASE	:       Union[str, 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'						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    self.tokenizer(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					return_tensors=self.framework    ,					truncation=_SCREAMING_SNAKE_CASE    ,					padding=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    table
					return inputs





	def  _lowerCAmelCase    (			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]=False						)	->  Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    model_inputs.pop('table'						)

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







	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					Any						)	->  Optional[Any]:





					"""simple docstring"""


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

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

													answers.append(_SCREAMING_SNAKE_CASE						)
									if len(_SCREAMING_SNAKE_CASE						) == 0:
													raise PipelineException('Empty answer'						)
					else:
									SCREAMING_SNAKE_CASE	:       Tuple 							=    [{'answer': answer} for answer in self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,					skip_special_tokens=_SCREAMING_SNAKE_CASE						)]

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










"""simple docstring"""

def    __snake_case       (  __A			:	str       ,		__A			:	str    )    ->					int:






				'''simple docstring'''
				# The longer word should come first
				if len(__A    ) < len(__A    ):
								return levenshtein_distance(__A       ,		__A    )

				if len(__A    ) == 0:
								return len(__A    )

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    list(range(len(__A    ) + 1    )    )

				for i, ca in enumerate(__A    ):
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [i + 1]

								for j, ca in enumerate(__A    ):
												# Calculate insertions, deletions and substitutions
												SCREAMING_SNAKE_CASE	:       str 							=    previous_row[j + 1] + 1
												SCREAMING_SNAKE_CASE	:       List[str] 							=    current_row[j] + 1
												SCREAMING_SNAKE_CASE	:       Any 							=    previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(__A       ,		__A       ,		__A    )    )

								# Store the previous row
								SCREAMING_SNAKE_CASE	:       Tuple 							=    current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
							A_	:						Optional[int]				=			input('Enter the first word:\n').strip()
							A_	:						Optional[Any]				=			input('Enter the second word:\n').strip()

							A_	:						str				=			levenshtein_distance(first_word, second_word)
							print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





