




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 				__a (			__lowerCamelCase					):


					"""simple docstring"""


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


									'''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(_UpperCamelCase    ,_UpperCamelCase						) and table.get("""query"""						) is not None and table.get("""table"""						) is not None:
																	SCREAMING_SNAKE_CASE__       =[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:
																					SCREAMING_SNAKE_CASE__       =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:
													SCREAMING_SNAKE_CASE__       =[{"""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__       =pd.DataFrame(tqa_pipeline_input["""table"""]						)

									return tqa_pipeline_inputs






@add_end_docstrings(__lowerCamelCase					)
class 				__a (			__lowerCamelCase					):


					"""simple docstring"""


					_A    :   Any     =  "table,query"




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


									'''simple docstring'''





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




					def   __A    (     self      :			List[str]    ,**_UpperCamelCase      :			int						)		->       List[Any]:


									'''simple docstring'''





									return self.model(**_UpperCamelCase						)




					def   __A    (     self      :			Tuple    ,**_UpperCamelCase      :			int						)		->       Dict:


									'''simple docstring'''





									if self.framework == "pt":
													SCREAMING_SNAKE_CASE__       =[]
													SCREAMING_SNAKE_CASE__       =[]
													SCREAMING_SNAKE_CASE__       =None
													SCREAMING_SNAKE_CASE__       =inputs["""input_ids"""].shape[0]

													SCREAMING_SNAKE_CASE__       =inputs["""input_ids"""].to(self.device						)
													SCREAMING_SNAKE_CASE__       =inputs["""attention_mask"""].to(self.device						)
													SCREAMING_SNAKE_CASE__       =inputs["""token_type_ids"""].to(self.device						)
													SCREAMING_SNAKE_CASE__       =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:
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 3]  # shape (seq_len,)
																					SCREAMING_SNAKE_CASE__       =np.zeros_like(prev_labels_example.cpu().numpy()						)  # shape (seq_len,)

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

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

																					SCREAMING_SNAKE_CASE__       =torch.from_numpy(_UpperCamelCase						).type(torch.long						).to(self.device						)

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

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

																	all_logits.append(_UpperCamelCase						)

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

																	SCREAMING_SNAKE_CASE__       =collections.defaultdict(_UpperCamelCase						)
																	for i, p in enumerate(probabilities.squeeze().tolist()						):
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 0].tolist()[i]
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 1].tolist()[i] - 1
																					SCREAMING_SNAKE_CASE__       =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						)

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

													SCREAMING_SNAKE_CASE__       =torch.cat(tuple(_UpperCamelCase						)    ,0						)

													return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_UpperCamelCase						)    ,0						))
									else:
													SCREAMING_SNAKE_CASE__       =[]
													SCREAMING_SNAKE_CASE__       =[]
													SCREAMING_SNAKE_CASE__       =None
													SCREAMING_SNAKE_CASE__       =inputs["""input_ids"""].shape[0]

													SCREAMING_SNAKE_CASE__       =inputs["""input_ids"""]
													SCREAMING_SNAKE_CASE__       =inputs["""attention_mask"""]
													SCREAMING_SNAKE_CASE__       =inputs["""token_type_ids"""].numpy()
													SCREAMING_SNAKE_CASE__       =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:
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 3]  # shape (seq_len,)
																					SCREAMING_SNAKE_CASE__       =np.zeros_like(_UpperCamelCase    ,dtype=np.intaa						)  # shape (seq_len,)

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

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

																					SCREAMING_SNAKE_CASE__       =model_labels

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

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

																	all_logits.append(_UpperCamelCase						)

																	SCREAMING_SNAKE_CASE__       =tfp.distributions.Bernoulli(logits=_UpperCamelCase						)
																	SCREAMING_SNAKE_CASE__       =dist_per_token.probs_parameter() * tf.cast(_UpperCamelCase    ,tf.floataa						)

																	SCREAMING_SNAKE_CASE__       =collections.defaultdict(_UpperCamelCase						)
																	SCREAMING_SNAKE_CASE__       =token_type_ids_example
																	for i, p in enumerate(tf.squeeze(_UpperCamelCase						).numpy().tolist()						):
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 0].tolist()[i]
																					SCREAMING_SNAKE_CASE__       =token_type_ids_example[:, 1].tolist()[i] - 1
																					SCREAMING_SNAKE_CASE__       =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						)

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

													SCREAMING_SNAKE_CASE__       =tf.concat(tuple(_UpperCamelCase						)    ,0						)

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




					def __call__(     self      :			Dict    ,*_UpperCamelCase      :			Optional[int]    ,**_UpperCamelCase      :			Union[str, Any]						)		->       Dict:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self._args_parser(*_UpperCamelCase    ,**_UpperCamelCase						)

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




					def   __A    (     self      :			Any    ,_UpperCamelCase      :			Optional[int]=None    ,_UpperCamelCase      :			Dict=None    ,_UpperCamelCase      :			Optional[int]=None    ,**_UpperCamelCase      :			str						)		->       Optional[Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       ={}
									if padding is not None:
													SCREAMING_SNAKE_CASE__       =padding
									if truncation is not None:
													SCREAMING_SNAKE_CASE__       =truncation

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




					def   __A    (     self      :			str    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			List[str]=True    ,_UpperCamelCase      :			Dict=None						)		->       Any:


									'''simple docstring'''





									if truncation is None:
													if self.type == "tapas":
																	SCREAMING_SNAKE_CASE__       ="""drop_rows_to_fit"""
													else:
																	SCREAMING_SNAKE_CASE__       ="""do_not_truncate"""

									SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =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__       =self.tokenizer(_UpperCamelCase    ,_UpperCamelCase    ,return_tensors=self.framework    ,truncation=_UpperCamelCase    ,padding=_UpperCamelCase						)
									SCREAMING_SNAKE_CASE__       =table
									return inputs




					def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Tuple=False						)		->       List[Any]:


									'''simple docstring'''





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

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




					def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			Optional[int]						)		->       Any:


									'''simple docstring'''





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

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

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

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












def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    # Create a stack and push index of fist element to it
    SCREAMING_SNAKE_CASE__       =[]
    st.append(0     )

    # Span value of first element is always 1
    SCREAMING_SNAKE_CASE__       =1

    # Calculate span values for rest of the elements
    for i in range(1,    __UpperCamelCase     ):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(__UpperCamelCase     ) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        SCREAMING_SNAKE_CASE__       =i + 1 if len(__UpperCamelCase     ) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    for i in range(0,    __UpperCamelCase     ):
        print(arr[i],    end=""" """     )


# Driver program to test above function
lowerCamelCase_  						=       [10, 4, 5, 90, 120, 80]
lowerCamelCase_  						=       [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)




print_array(S, len(price))






