

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      snake_case_      (					lowerCAmelCase_				):


							"""simple docstring"""





							def __call__(			self       ,    _A=None       ,    _A=None       ,    **_A				):
												# Returns tqa_pipeline_inputs of shape:
												# [
												#   {"table": pd.DataFrame, "query": List[str]},
												#   ...,
												#   {"table": pd.DataFrame, "query" : List[str]}
												# ]
												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(_A       ,    _A				) and table.get('query'				) is not None and table.get('table'				) is not None:
																						__lowerCAmelCase       =				[table]
																	elif isinstance(_A       ,    _A				) and len(_A				) > 0:
																						if not all(isinstance(_A       ,    _A				) for d in table				):
																											raise ValueError(
																											    F"""Keyword argument `table` should be a list of dict, but is {(type(_A				) for d in table)}"""				)

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

																						__lowerCAmelCase       =				pd.DataFrame(tqa_pipeline_input['table']				)

												return tqa_pipeline_inputs





@add_end_docstrings(lowerCAmelCase_				)
class      snake_case_      (					lowerCAmelCase_				):


							"""simple docstring"""





							__UpperCAmelCase      ="""table,query"""




							def __init__(			self       ,    _A=TableQuestionAnsweringArgumentHandler()       ,    *_A       ,    **_A				):
												super().__init__(*_A       ,    **_A				)
												__lowerCAmelCase       =				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()				)				)

												__lowerCAmelCase       =				bool(getattr(self.model.config       ,    'aggregation_labels'       ,    _A				)				) and bool(
												    getattr(self.model.config       ,    'num_aggregation_labels'       ,    _A				)				)
												__lowerCAmelCase       =				'''tapas''' if hasattr(self.model.config       ,    'aggregation_labels'				) else None




							def 							A__	(			self       ,    **_A				):
												return self.model(**_A				)




							def 							A__	(			self       ,    **_A				):
												if self.framework == "pt":
																	__lowerCAmelCase       =				[]
																	__lowerCAmelCase       =				[]
																	__lowerCAmelCase       =				None
																	__lowerCAmelCase       =				inputs['''input_ids'''].shape[0]

																	__lowerCAmelCase       =				inputs['''input_ids'''].to(self.device				)
																	__lowerCAmelCase       =				inputs['''attention_mask'''].to(self.device				)
																	__lowerCAmelCase       =				inputs['''token_type_ids'''].to(self.device				)
																	__lowerCAmelCase       =				None

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

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

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

																											__lowerCAmelCase       =				torch.from_numpy(_A				).type(torch.long				).to(self.device				)

																						__lowerCAmelCase       =				input_ids[index]
																						__lowerCAmelCase       =				attention_mask[index]  # shape (seq_len,)
																						__lowerCAmelCase       =				token_type_ids[index]  # shape (seq_len, 7)
																						__lowerCAmelCase       =				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				)       ,    )
																						__lowerCAmelCase       =				outputs.logits

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

																						all_logits.append(_A				)

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

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

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

																	__lowerCAmelCase       =				torch.cat(tuple(_A				)       ,    0				)

																	return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_A				)       ,    0				))
												else:
																	__lowerCAmelCase       =				[]
																	__lowerCAmelCase       =				[]
																	__lowerCAmelCase       =				None
																	__lowerCAmelCase       =				inputs['''input_ids'''].shape[0]

																	__lowerCAmelCase       =				inputs['''input_ids''']
																	__lowerCAmelCase       =				inputs['''attention_mask''']
																	__lowerCAmelCase       =				inputs['''token_type_ids'''].numpy()
																	__lowerCAmelCase       =				None

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

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

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

																											__lowerCAmelCase       =				model_labels

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

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

																						all_logits.append(_A				)

																						__lowerCAmelCase       =				tfp.distributions.Bernoulli(logits=_A				)
																						__lowerCAmelCase       =				dist_per_token.probs_parameter() * tf.cast(_A       ,    tf.floataa				)

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

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

																	__lowerCAmelCase       =				tf.concat(tuple(_A				)       ,    0				)

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




							def __call__(			self       ,    *_A       ,    **_A				):
												__lowerCAmelCase       =				self._args_parser(*_A       ,    **_A				)

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




							def 							A__	(			self       ,    _A=None       ,    _A=None       ,    _A=None       ,    **_A				):
												__lowerCAmelCase       =				{}
												if padding is not None:
																	__lowerCAmelCase       =				padding
												if truncation is not None:
																	__lowerCAmelCase       =				truncation

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




							def 							A__	(			self       ,    _A       ,    _A=None       ,    _A=True       ,    _A=None				):
												if truncation is None:
																	if self.type == "tapas":
																						__lowerCAmelCase       =				'''drop_rows_to_fit'''
																	else:
																						__lowerCAmelCase       =				'''do_not_truncate'''

												__lowerCAmelCase       =				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'				)
												__lowerCAmelCase       =				self.tokenizer(_A       ,    _A       ,    return_tensors=self.framework       ,    truncation=_A       ,    padding=_A				)
												__lowerCAmelCase       =				table
												return inputs




							def 							A__	(			self       ,    _A       ,    _A=False				):
												__lowerCAmelCase       =				model_inputs.pop('table'				)

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




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

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

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

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











"""simple docstring"""






_lowercase					:					str 		=		{
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

_lowercase					:					Dict 		=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_lowercase					:					Union[str, Any] 		=		[0 for x in range(len(graph))]
_lowercase					:					Union[str, Any] 		=		[]






def 	snake_case__  (						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Dict      ):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =1
			while stack:
						lowerCamelCase__						:						int						      =stack.pop()
						print(__lowerCamelCase				,						clothes[current_clothing]      )
						order += 1






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       str				,						__lowerCamelCase							:       Tuple      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =1
			for v in graph[u]:
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			stack.append(__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Optional[Any]				,						__lowerCamelCase							:       Optional[Any]      ):
			"""simple docstring"""
			for v in range(len(__lowerCamelCase      )      ):
						if not visited[v]:
									depth_first_search(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )


if __name__ == "__main__":
		topological_sort(graph, visited)
		print(stack)
		print_stack(stack, clothes)




