




"""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        UpperCAmelCase_  (	SCREAMING_SNAKE_CASE_						):
		def __call__(		self							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		**UpperCamelCase_   )		->   Any:
					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:
											__lowercase	:       Optional[int]				=   [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:
														__lowercase	:       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(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:
								__lowercase	:       Tuple				=   [{'''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	:       Union[str, Any]				=   pd.DataFrame(tqa_pipeline_input['''table''']   )

					return tqa_pipeline_inputs


@add_end_docstrings(SCREAMING_SNAKE_CASE_						)
class        UpperCAmelCase_  (	SCREAMING_SNAKE_CASE_						):
		UpperCamelCase									="table,query"
		def __init__(		self							,		UpperCamelCase_=TableQuestionAnsweringArgumentHandler()							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   Any:
					super().__init__(*SCREAMING_SNAKE_CASE_							,		**SCREAMING_SNAKE_CASE_   )
					__lowercase	:       List[str]				=   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	:       Optional[int]				=   bool(getattr(self.model.config							,		'''aggregation_labels'''							,		SCREAMING_SNAKE_CASE_   )   ) and bool(
					    getattr(self.model.config							,		'''num_aggregation_labels'''							,		SCREAMING_SNAKE_CASE_   )   )
					__lowercase	:       Any				=   '''tapas''' if hasattr(self.model.config							,		'''aggregation_labels'''   ) else None
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   Union[str, Any]:
					return self.model(**SCREAMING_SNAKE_CASE_   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   List[str]:






					if self.framework == "pt":
								__lowercase	:       List[Any]				=   []
								__lowercase	:       List[str]				=   []
								__lowercase	:       Union[str, Any]				=   None
								__lowercase	:       int				=   inputs['''input_ids'''].shape[0]

								__lowercase	:       Union[str, Any]				=   inputs['''input_ids'''].to(self.device   )
								__lowercase	:       int				=   inputs['''attention_mask'''].to(self.device   )
								__lowercase	:       Optional[int]				=   inputs['''token_type_ids'''].to(self.device   )
								__lowercase	:       Union[str, Any]				=   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:
														__lowercase	:       Tuple				=   token_type_ids_example[:, 3]  # shape (seq_len,)
														__lowercase	:       Union[str, Any]				=   np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

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

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

														__lowercase	:       Any				=   torch.from_numpy(SCREAMING_SNAKE_CASE_   ).type(torch.long   ).to(self.device   )

											__lowercase	:       List[str]				=   input_ids[index]
											__lowercase	:       List[str]				=   attention_mask[index]  # shape (seq_len,)
											__lowercase	:       Union[str, Any]				=   token_type_ids[index]  # shape (seq_len, 7)
											__lowercase	:       List[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   )							,		)
											__lowercase	:       Tuple				=   outputs.logits

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

											all_logits.append(SCREAMING_SNAKE_CASE_   )

											__lowercase	:       Tuple				=   torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_   )
											__lowercase	:       Dict				=   dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
											    dist_per_token.probs.device   )

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

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

								__lowercase	:       Dict				=   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:
								__lowercase	:       Tuple				=   []
								__lowercase	:       Dict				=   []
								__lowercase	:       Optional[int]				=   None
								__lowercase	:       str				=   inputs['''input_ids'''].shape[0]

								__lowercase	:       str				=   inputs['''input_ids''']
								__lowercase	:       List[Any]				=   inputs['''attention_mask''']
								__lowercase	:       Tuple				=   inputs['''token_type_ids'''].numpy()
								__lowercase	:       Optional[Any]				=   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:
														__lowercase	:       Any				=   token_type_ids_example[:, 3]  # shape (seq_len,)
														__lowercase	:       List[Any]				=   np.zeros_like(SCREAMING_SNAKE_CASE_							,		dtype=np.intaa   )  # shape (seq_len,)

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

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

														__lowercase	:       Dict				=   model_labels

											__lowercase	:       Optional[int]				=   input_ids[index]
											__lowercase	:       str				=   attention_mask[index]  # shape (seq_len,)
											__lowercase	:       Any				=   token_type_ids[index]  # shape (seq_len, 7)
											__lowercase	:       Any				=   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   )							,		)
											__lowercase	:       List[Any]				=   outputs.logits

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

											all_logits.append(SCREAMING_SNAKE_CASE_   )

											__lowercase	:       Tuple				=   tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_   )
											__lowercase	:       List[str]				=   dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE_							,		tf.floataa   )

											__lowercase	:       int				=   collections.defaultdict(SCREAMING_SNAKE_CASE_   )
											__lowercase	:       Any				=   token_type_ids_example
											for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE_   ).numpy().tolist()   ):
														__lowercase	:       List[str]				=   token_type_ids_example[:, 0].tolist()[i]
														__lowercase	:       List[str]				=   token_type_ids_example[:, 1].tolist()[i] - 1
														__lowercase	:       List[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_   )

											__lowercase	:       Union[str, Any]				=   {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

								__lowercase	:       List[Any]				=   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							,		*UpperCamelCase_							,		**UpperCamelCase_   )		->   str:
					__lowercase	:       Union[str, Any]				=   self._args_parser(*SCREAMING_SNAKE_CASE_							,		**SCREAMING_SNAKE_CASE_   )

					__lowercase	:       Tuple				=   super().__call__(SCREAMING_SNAKE_CASE_							,		**SCREAMING_SNAKE_CASE_   )
					if len(SCREAMING_SNAKE_CASE_   ) == 1:
								return results[0]
					return results
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		UpperCamelCase_=None							,		**UpperCamelCase_   )		->   Optional[int]:
					__lowercase	:       Any				=   {}
					if padding is not None:
								__lowercase	:       Tuple				=   padding
					if truncation is not None:
								__lowercase	:       List[str]				=   truncation

					__lowercase	:       Any				=   {}
					if sequential is not None:
								__lowercase	:       Any				=   sequential
					return preprocess_params, forward_params, {}
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=None							,		UpperCamelCase_=True							,		UpperCamelCase_=None   )		->   str:






					if truncation is None:
								if self.type == "tapas":
											__lowercase	:       str				=   '''drop_rows_to_fit'''
								else:
											__lowercase	:       str				=   '''do_not_truncate'''

					__lowercase	,__lowercase	:       Optional[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'''   )
					__lowercase	:       Optional[Any]				=   self.tokenizer(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		return_tensors=self.framework							,		truncation=SCREAMING_SNAKE_CASE_							,		padding=SCREAMING_SNAKE_CASE_   )
					__lowercase	:       Tuple				=   table
					return inputs
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=False   )		->   Optional[Any]:
					__lowercase	:       Dict				=   model_inputs.pop('''table'''   )

					if self.type == "tapas":
								if sequential:
											__lowercase	:       List[str]				=   self.sequential_inference(**SCREAMING_SNAKE_CASE_   )
								else:
											__lowercase	:       int				=   self.batch_inference(**SCREAMING_SNAKE_CASE_   )
					else:
								__lowercase	:       Any				=   self.model.generate(**SCREAMING_SNAKE_CASE_   )
					__lowercase	:       Union[str, Any]				=   {'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
					return model_outputs
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   Tuple:
					__lowercase	:       List[Any]				=   model_outputs['''model_inputs''']
					__lowercase	:       Any				=   model_outputs['''table''']
					__lowercase	:       int				=   model_outputs['''outputs''']
					if self.type == "tapas":
								if self.aggregate:
											__lowercase	,__lowercase	:       int				=   outputs[:2]
											__lowercase	:       Optional[Any]				=   self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_   )
											__lowercase	,__lowercase	:       Tuple				=   predictions
											__lowercase	:       Dict				=   {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(SCREAMING_SNAKE_CASE_   )}

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

											answers.append(SCREAMING_SNAKE_CASE_   )
								if len(SCREAMING_SNAKE_CASE_   ) == 0:
											raise PipelineException('''Empty answer'''   )
					else:
								__lowercase	:       List[str]				=   [{'''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"""




from __future__ import annotations







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       List[Any]				=   [0] * len(__UpperCamelCase					)
			for i in range(1					,							len(__UpperCamelCase					)					):
						__lowercase	:       Optional[Any]				=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					):
			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(__UpperCamelCase					)
			]







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return sum(__UpperCamelCase					) / len(__UpperCamelCase					)


if __name__ == "__main__":
			# process id's
			a_            =   [1, 2, 3]

			# ensure that we actually have processes
			if len(processes) == 0:
						print('Zero amount of processes')
						raise SystemExit(0)

			# duration time of all processes
			a_            =   [1_9, 8, 9]

			# ensure we can match each id to a duration time
			if len(duration_times) != len(processes):
						print('Unable to match all id\'s with their duration time')
						raise SystemExit(0)

			# get the waiting times and the turnaround times
			a_            =   calculate_waiting_times(duration_times)
			a_            =   calculate_turnaround_times(duration_times, waiting_times)

			# get the average times
			a_            =   calculate_average_waiting_time(waiting_times)
			a_            =   calculate_average_turnaround_time(turnaround_times)

			# print all the results
			print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
			for i, process in enumerate(processes):
						print(
						    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
						    F"{turnaround_times[i]}"
						)
			print(F"Average waiting time = {average_waiting_time}")
			print(F"Average turn around time = {average_turnaround_time}")



