"""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     (       a__		):



				def __call__(self							: Union[str, Any]      ,    _A							: Union[str, Any]=None      ,    _A							: List[Any]=None      ,    **_A							: str)       ->      List[Any]:
					# 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(lowercase__      ,    lowercase__) and table.get('query') is not None and table.get('table') is not None:
							__snake_case     :				Optional[Any] 						=					[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:
								__snake_case     :				str 						=					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:
						__snake_case     :				str 						=					[{'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.')

							__snake_case     :				Dict 						=					pd.DataFrame(tqa_pipeline_input['table'])

					return tqa_pipeline_inputs







@add_end_docstrings(a__		)
class 						UpperCamelCase     (       a__		):
				UpperCAmelCase							: Any       =						"""table,query"""



				def __init__(self							: Tuple      ,    _A							: Dict=TableQuestionAnsweringArgumentHandler()      ,    *_A							: Tuple      ,    **_A							: List[Any])       ->      Dict:
					super().__init__(*lowercase__      ,    **lowercase__)
					__snake_case     :				int 						=					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()))

					__snake_case     :				List[str] 						=					bool(getattr(self.model.config      ,    'aggregation_labels'      ,    lowercase__)) and bool(
					    getattr(self.model.config      ,    'num_aggregation_labels'      ,    lowercase__))
					__snake_case     :				Tuple 						=					'tapas' if hasattr(self.model.config      ,    'aggregation_labels') else None



				def    _lowercase	(self							: int      ,    **_A							: Any)       ->      Any:
					return self.model(**lowercase__)



				def    _lowercase	(self							: List[Any]      ,    **_A							: Tuple)       ->      List[str]:
					if self.framework == "pt":
						__snake_case     :				int 						=					[]
						__snake_case     :				Optional[Any] 						=					[]
						__snake_case     :				List[str] 						=					None
						__snake_case     :				Tuple 						=					inputs['input_ids'].shape[0]

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

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

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

								__snake_case     :				Tuple 						=					torch.from_numpy(lowercase__).type(torch.long).to(self.device)

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

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

							all_logits.append(lowercase__)

							__snake_case     :				Dict 						=					torch.distributions.Bernoulli(logits=lowercase__)
							__snake_case     :				Union[str, Any] 						=					dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
							    dist_per_token.probs.device)

							__snake_case     :				Optional[int] 						=					collections.defaultdict(lowercase__)
							for i, p in enumerate(probabilities.squeeze().tolist()):
								__snake_case     :				Optional[Any] 						=					token_type_ids_example[:, 0].tolist()[i]
								__snake_case     :				Optional[Any] 						=					token_type_ids_example[:, 1].tolist()[i] - 1
								__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(lowercase__)

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

						__snake_case     :				List[str] 						=					torch.cat(tuple(lowercase__)      ,    0)

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

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

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

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

								__snake_case     :				Optional[int] 						=					model_labels

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

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

							all_logits.append(lowercase__)

							__snake_case     :				Dict 						=					tfp.distributions.Bernoulli(logits=lowercase__)
							__snake_case     :				List[str] 						=					dist_per_token.probs_parameter() * tf.cast(lowercase__      ,    tf.floataa)

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

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

						__snake_case     :				Dict 						=					tf.concat(tuple(lowercase__)      ,    0)

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



				def __call__(self							: Union[str, Any]      ,    *_A							: str      ,    **_A							: Optional[Any])       ->      Optional[int]:
					__snake_case     :				List[Any] 						=					self._args_parser(*lowercase__      ,    **lowercase__)

					__snake_case     :				int 						=					super().__call__(lowercase__      ,    **lowercase__)
					if len(lowercase__) == 1:
						return results[0]
					return results



				def    _lowercase	(self							: Tuple      ,    _A							: int=None      ,    _A							: Optional[Any]=None      ,    _A							: int=None      ,    **_A							: Union[str, Any])       ->      Union[str, Any]:
					__snake_case     :				str 						=					{}
					if padding is not None:
						__snake_case     :				Optional[Any] 						=					padding
					if truncation is not None:
						__snake_case     :				int 						=					truncation

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



				def    _lowercase	(self							: List[str]      ,    _A							: Tuple      ,    _A							: str=None      ,    _A							: Any=True      ,    _A							: List[str]=None)       ->      Any:
					if truncation is None:
						if self.type == "tapas":
							__snake_case     :				Tuple 						=					'drop_rows_to_fit'
						else:
							__snake_case     :				str 						=					'do_not_truncate'

					__snake_case						,							__snake_case     :				int 						=					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')
					__snake_case     :				Optional[Any] 						=					self.tokenizer(lowercase__      ,    lowercase__      ,    return_tensors=self.framework      ,    truncation=lowercase__      ,    padding=lowercase__)
					__snake_case     :				Union[str, Any] 						=					table
					return inputs



				def    _lowercase	(self							: Optional[int]      ,    _A							: Tuple      ,    _A							: Union[str, Any]=False)       ->      Optional[int]:
					__snake_case     :				List[Any] 						=					model_inputs.pop('table')

					if self.type == "tapas":
						if sequential:
							__snake_case     :				Union[str, Any] 						=					self.sequential_inference(**lowercase__)
						else:
							__snake_case     :				Optional[Any] 						=					self.batch_inference(**lowercase__)
					else:
						__snake_case     :				Optional[int] 						=					self.model.generate(**lowercase__)
					__snake_case     :				Union[str, Any] 						=					{'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
					return model_outputs






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

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

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

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











import math






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  math.log(2      ,      10			) * number
  a_		    =  round((log_number - int(_A			))      ,      15			)

  return difference






def 				UpperCAmelCase__  (							_A = 678_910			):






  """simple docstring"""


  a_		    =  90
  a_		    =  0

  a_		    =  math.log(1.23      ,      10			)
  a_		    =  math.log(1.24      ,      10			)
  a_		    =  0

  while position < number:
    a_		    =  log_difference(_A			)

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 196

    else:
      a_		    =  power_iterator
      power_iterator += 196
      position += 1

  return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")
