



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 (			_UpperCAmelCase      ):






			"""simple docstring"""





			def __call__(					self						:							Tuple      ,  lowercase						:							Tuple=None      ,  lowercase						:							str=None      ,  **lowercase						:							Tuple )    ->       Dict:
									"""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(lowercase      ,  lowercase ) and table.get("""query""" ) is not None and table.get("""table""" ) is not None:
																					__lowercase											=					[table]
															elif isinstance(lowercase      ,  lowercase ) and len(lowercase ) > 0:
																					if not all(isinstance(lowercase      ,  lowercase ) for d in table ):
																											raise ValueError(
																											    F"Keyword argument `table` should be a list of dict, but is {(type(lowercase ) 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(lowercase      ,  lowercase ) or isinstance(lowercase      ,  types.GeneratorType ):
																					return table
															else:
																					raise ValueError(
																					    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																					    F"is {type(lowercase )})" )
									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       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	str  			=      """table,query"""





			def __init__(					self						:							Tuple      ,  lowercase						:							Optional[int]=TableQuestionAnsweringArgumentHandler()      ,  *lowercase						:							Any      ,  **lowercase						:							Optional[int] )    ->       Optional[int]:
									"""simple docstring"""







									super().__init__(*lowercase      ,  **lowercase )
									__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"""      ,  lowercase ) ) and bool(
									    getattr(self.model.config      ,  """num_aggregation_labels"""      ,  lowercase ) )
									__lowercase											=					"""tapas""" if hasattr(self.model.config      ,  """aggregation_labels""" ) else None





			def 		snake_case__      (					self						:							Optional[int]      ,  **lowercase						:							Any )    ->       List[Any]:
									"""simple docstring"""







									return self.model(**lowercase )





			def 		snake_case__      (					self						:							Dict      ,  **lowercase						:							Dict )    ->       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(lowercase ):
																					# 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(lowercase ).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(lowercase )

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

																					__lowercase											=					collections.defaultdict(lowercase )
																					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(lowercase )

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

															__lowercase											=					torch.cat(tuple(lowercase )      ,  0 )

															return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(lowercase )      ,  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(lowercase ):
																					# 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(lowercase      ,  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(lowercase      ,  axis=0 )      ,  attention_mask=np.expand_dims(lowercase      ,  axis=0 )      ,  token_type_ids=np.expand_dims(lowercase      ,  axis=0 )      ,  )
																					__lowercase											=					outputs.logits

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

																					all_logits.append(lowercase )

																					__lowercase											=					tfp.distributions.Bernoulli(logits=lowercase )
																					__lowercase											=					dist_per_token.probs_parameter() * tf.cast(lowercase      ,  tf.floataa )

																					__lowercase											=					collections.defaultdict(lowercase )
																					__lowercase											=					token_type_ids_example
																					for i, p in enumerate(tf.squeeze(lowercase ).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(lowercase )

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

															__lowercase											=					tf.concat(tuple(lowercase )      ,  0 )

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





			def __call__(					self						:							Tuple      ,  *lowercase						:							Optional[Any]      ,  **lowercase						:							List[str] )    ->       int:
									"""simple docstring"""







									__lowercase											=					self._args_parser(*lowercase      ,  **lowercase )

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





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							Optional[Any]=None      ,  lowercase						:							int=None      ,  lowercase						:							Union[str, Any]=None      ,  **lowercase						:							Union[str, Any] )    ->       Dict:
									"""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 		snake_case__      (					self						:							List[Any]      ,  lowercase						:							int      ,  lowercase						:							Any=None      ,  lowercase						:							List[str]=True      ,  lowercase						:							Optional[Any]=None )    ->       Tuple:
									"""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(lowercase      ,  lowercase      ,  return_tensors=self.framework      ,  truncation=lowercase      ,  padding=lowercase )
									__lowercase											=					table
									return inputs





			def 		snake_case__      (					self						:							Any      ,  lowercase						:							Union[str, Any]      ,  lowercase						:							Union[str, Any]=False )    ->       int:
									"""simple docstring"""







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

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







			def 		snake_case__      (					self						:							Any      ,  lowercase						:							str )    ->       Optional[int]:
									"""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(lowercase      ,  lowercase      ,  lowercase )
																					__lowercase						,      __lowercase											=					predictions
																					__lowercase											=					{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(lowercase )}

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

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

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










from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase__			=    TypeVar("T")
class       _lowerCAmelCase (			Generic[T]      ):






			"""simple docstring"""





			def __init__(					self						:							int      ,  lowercase						:							T )    ->       Union[str, Any]:
									"""simple docstring"""







									__lowercase											=					data  # Assign data
									__lowercase											=					None  # Initialize next as null
									__lowercase											=					None  # Initialize prev as null





class       _lowerCAmelCase (			Generic[T]      ):






			"""simple docstring"""





			def __init__(					self						:							Any )    ->       None:
									"""simple docstring"""







									__lowercase											=					None





			def 		snake_case__      (					self						:							str      ,  lowercase						:							T )    ->       None:
									"""simple docstring"""







									if self.head is None:
															__lowercase											=					Node(lowercase )
									else:
															__lowercase											=					Node(lowercase )
															__lowercase											=					new_node
															__lowercase											=					self.head
															__lowercase											=					None
															__lowercase											=					new_node





			def 		snake_case__      (					self						:							Optional[Any] )    ->       T | None:
									"""simple docstring"""







									if self.head is None:
															return None
									else:
															assert self.head is not None
															__lowercase											=					self.head.data
															__lowercase											=					self.head.next
															if self.head is not None:
																					__lowercase											=					None
															return temp





			def 		snake_case__      (					self						:							Optional[Any] )    ->       T | None:
									"""simple docstring"""







									return self.head.data if self.head is not None else None





			def __len__(					self						:							List[str] )    ->       int:
									"""simple docstring"""







									__lowercase											=					self.head
									__lowercase											=					0
									while temp is not None:
															count += 1
															__lowercase											=					temp.next
									return count





			def 		snake_case__      (					self						:							Optional[Any] )    ->       bool:
									"""simple docstring"""







									return self.head is None







			def 		snake_case__      (					self						:							Optional[Any] )    ->       None:
									"""simple docstring"""







									print("""stack elements are:""" )
									__lowercase											=					self.head
									while temp is not None:
															print(temp.data      ,  end="""->""" )
															__lowercase											=					temp.next


# Code execution starts here
if __name__ == "__main__":
			# Start with the empty stack
			UpperCamelCase__			=    Stack()

			# Insert 4 at the beginning. So stack becomes 4->None
			print("Stack operations using Doubly LinkedList")
			stack.push(4)

			# Insert 5 at the beginning. So stack becomes 4->5->None
			stack.push(5)

			# Insert 6 at the beginning. So stack becomes 4->5->6->None
			stack.push(6)

			# Insert 7 at the beginning. So stack becomes 4->5->6->7->None
			stack.push(7)

			# Print the stack
			stack.print_stack()

			# Print the top element
			print("\nTop element is ", stack.top())

			# Print the stack size
			print("Size of the stack is ", len(stack))

			# pop the top element
			stack.pop()

			# pop the top element
			stack.pop()

			# two elements have now been popped off
			stack.print_stack()

			# Print True if the stack is empty else False
			print("\nstack is empty:", stack.is_empty())





