

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_			(				_UpperCamelCase	):



      """simple docstring"""







      def __call__( self	:				Tuple     ,snake_case	:				Optional[Any]=None     ,snake_case	:				str=None     ,**snake_case	:				Optional[int]							):
           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:
                     SCREAMING_SNAKE_CASE 		=[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:
                          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(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:
                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(_UpperCamelCase	)
class 						a_			(				_UpperCamelCase	):



      """simple docstring"""







      __UpperCAmelCase     						= 'table,query'
      def __init__( self	:				Dict     ,snake_case	:				List[Any]=TableQuestionAnsweringArgumentHandler()     ,*snake_case	:				Dict     ,**snake_case	:				List[Any]							):
           super().__init__(*a__     ,**a__							)
           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'     ,a__							)							) and bool(
               getattr(self.model.config     ,'num_aggregation_labels'     ,a__							)							)
           SCREAMING_SNAKE_CASE 		='tapas' if hasattr(self.model.config     ,'aggregation_labels'							) else None
      def 			_lowerCAmelCase	( self	:				str     ,**snake_case	:				List[str]							):
           return self.model(**a__							)
      def 			_lowerCAmelCase	( self	:				int     ,**snake_case	:				Optional[int]							):







           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(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:
                          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(a__							).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(a__							)

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

                     SCREAMING_SNAKE_CASE 		=collections.defaultdict(a__							)
                     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(a__							)

                     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(a__							)     ,0							)

                return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a__							)     ,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(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:
                          SCREAMING_SNAKE_CASE 		=token_type_ids_example[:, 3]  # shape (seq_len,)
                          SCREAMING_SNAKE_CASE 		=np.zeros_like(a__     ,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(a__     ,axis=0							)     ,attention_mask=np.expand_dims(a__     ,axis=0							)     ,token_type_ids=np.expand_dims(a__     ,axis=0							)     ,)
                     SCREAMING_SNAKE_CASE 		=outputs.logits

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

                     all_logits.append(a__							)

                     SCREAMING_SNAKE_CASE 		=tfp.distributions.Bernoulli(logits=a__							)
                     SCREAMING_SNAKE_CASE 		=dist_per_token.probs_parameter() * tf.cast(a__     ,tf.floataa							)

                     SCREAMING_SNAKE_CASE 		=collections.defaultdict(a__							)
                     SCREAMING_SNAKE_CASE 		=token_type_ids_example
                     for i, p in enumerate(tf.squeeze(a__							).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(a__							)

                     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(a__							)     ,0							)

                return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(a__							)     ,0							))
      def __call__( self	:				List[Any]     ,*snake_case	:				Optional[int]     ,**snake_case	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=self._args_parser(*a__     ,**a__							)

           SCREAMING_SNAKE_CASE 		=super().__call__(a__     ,**a__							)
           if len(a__							) == 1:
                return results[0]
           return results
      def 			_lowerCAmelCase	( self	:				List[str]     ,snake_case	:				Any=None     ,snake_case	:				Dict=None     ,snake_case	:				List[str]=None     ,**snake_case	:				List[str]							):
           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 			_lowerCAmelCase	( self	:				Union[str, Any]     ,snake_case	:				Optional[int]     ,snake_case	:				Any=None     ,snake_case	:				Dict=True     ,snake_case	:				int=None							):







           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(a__     ,a__     ,return_tensors=self.framework     ,truncation=a__     ,padding=a__							)
           SCREAMING_SNAKE_CASE 		=table
           return inputs
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				int     ,snake_case	:				Dict=False							):
           SCREAMING_SNAKE_CASE 		=model_inputs.pop('table'							)

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


      def 			_lowerCAmelCase	( self	:				str     ,snake_case	:				List[str]							):
           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(a__     ,a__     ,a__							)
                     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=predictions
                     SCREAMING_SNAKE_CASE 		={i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(a__							)}

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

                     answers.append(a__							)
                if len(a__							) == 0:
                     raise PipelineException('Empty answer'							)
           else:
                SCREAMING_SNAKE_CASE 		=[{'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"""


def   a__			(					__lowercase )						->						bool:
				# must be
				_A       	=      [list(__lowercase ) for row in matrix]
				if matrix and isinstance(__lowercase  ,      __lowercase ):
								if isinstance(matrix[0]  ,      __lowercase ):
												_A       	=      0
												for row in matrix:
																if prev_len == 0:
																				_A       	=      len(__lowercase )
																				_A       	=      True
																else:
																				_A       	=      prev_len == len(__lowercase )
								else:
												_A       	=      True
				else:
								_A       	=      False

				return result






def   a__			(					__lowercase )						->						None:
				if check_matrix(__lowercase ) and len(__lowercase ) > 0:
								_A       	=      [list(__lowercase ) for row in a]
								_A       	=      len(__lowercase )
								if isinstance(a[0]  ,      __lowercase ):
												_A       	=      len(a[0] )
								else:
												for dat in a:
																print(__lowercase )
												return

								# horizotal printing increasing
								for i in range(0  ,      __lowercase ):
												print(a[0][i] )
								# vertical printing down
								for i in range(1  ,      __lowercase ):
												print(a[i][mat_col - 1] )
								# horizotal printing decreasing
								if mat_row > 1:
												for i in range(mat_col - 2  ,      -1  ,      -1 ):
																print(a[mat_row - 1][i] )
        # vertical printing up
								for i in range(mat_row - 2  ,      0  ,      -1 ):
												print(a[i][0] )
								_A       	=      [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
								if len(__lowercase ) > 0:
												spiral_print_clockwise(__lowercase )
								else:
												return
				else:
								print("Not a valid matrix" )
								return






def   a__			(					__lowercase )						->						list[int]:
				if matrix:
								return list(matrix.pop(0 ) ) + spiral_traversal(list(zip(*__lowercase ) )[::-1] )
				else:
								return []


# driver code
if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a_											=     [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
		spiral_print_clockwise(a)