
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__	(			__SCREAMING_SNAKE_CASE):
      def __call__(   self					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):
       # 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(__magic_name__					,						__magic_name__   ) and table.get("""query"""   ) is not None and table.get("""table"""   ) is not None:
         lowerCamelCase	:			Optional[Any]           =   [table]
        elif isinstance(__magic_name__					,						__magic_name__   ) and len(__magic_name__   ) > 0:
         if not all(isinstance(__magic_name__					,						__magic_name__   ) for d in table   ):
          raise ValueError(
              F'''Keyword argument `table` should be a list of dict, but is {(type(__magic_name__   ) for d in table)}'''   )

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

         lowerCamelCase	:			int           =   pd.DataFrame(tqa_pipeline_input["""table"""]   )

       return tqa_pipeline_inputs





@add_end_docstrings(__SCREAMING_SNAKE_CASE)
class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Any					 =    """table,query"""
      def __init__(   self					,						__magic_name__=TableQuestionAnsweringArgumentHandler()					,						*__magic_name__					,						**__magic_name__   ):
       super().__init__(*__magic_name__					,						**__magic_name__   )
       lowerCamelCase	:			Any           =   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	:			str           =   bool(getattr(self.model.config					,						"""aggregation_labels"""					,						__magic_name__   )   ) and bool(
           getattr(self.model.config					,						"""num_aggregation_labels"""					,						__magic_name__   )   )
       lowerCamelCase	:			Optional[Any]           =   """tapas""" if hasattr(self.model.config					,						"""aggregation_labels"""   ) else None
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       return self.model(**__magic_name__   )
      def 		UpperCamelCase__							(   self					,						**__magic_name__   ):
       if self.framework == "pt":
        lowerCamelCase	:			Dict           =   []
        lowerCamelCase	:			Dict           =   []
        lowerCamelCase	:			Union[str, Any]           =   None
        lowerCamelCase	:			List[str]           =   inputs["""input_ids"""].shape[0]

        lowerCamelCase	:			List[Any]           =   inputs["""input_ids"""].to(self.device   )
        lowerCamelCase	:			List[str]           =   inputs["""attention_mask"""].to(self.device   )
        lowerCamelCase	:			str           =   inputs["""token_type_ids"""].to(self.device   )
        lowerCamelCase	:			Tuple           =   None

        for index in range(__magic_name__   ):
         # 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	:			Dict           =   token_type_ids_example[:, 3]  # shape (seq_len,)
          lowerCamelCase	:			int           =   np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

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

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

          lowerCamelCase	:			Optional[Any]           =   torch.from_numpy(__magic_name__   ).type(torch.long   ).to(self.device   )

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

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

         all_logits.append(__magic_name__   )

         lowerCamelCase	:			Optional[int]           =   torch.distributions.Bernoulli(logits=__magic_name__   )
         lowerCamelCase	:			Optional[int]           =   dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
             dist_per_token.probs.device   )

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

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

        lowerCamelCase	:			int           =   torch.cat(tuple(__magic_name__   )					,						0   )

        return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__magic_name__   )					,						0   ))
       else:
        lowerCamelCase	:			Optional[Any]           =   []
        lowerCamelCase	:			int           =   []
        lowerCamelCase	:			Optional[int]           =   None
        lowerCamelCase	:			int           =   inputs["""input_ids"""].shape[0]

        lowerCamelCase	:			Union[str, Any]           =   inputs["""input_ids"""]
        lowerCamelCase	:			List[str]           =   inputs["""attention_mask"""]
        lowerCamelCase	:			Union[str, Any]           =   inputs["""token_type_ids"""].numpy()
        lowerCamelCase	:			Union[str, Any]           =   None

        for index in range(__magic_name__   ):
         # 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	:			Optional[Any]           =   token_type_ids_example[:, 3]  # shape (seq_len,)
          lowerCamelCase	:			Union[str, Any]           =   np.zeros_like(__magic_name__					,						dtype=np.intaa   )  # shape (seq_len,)

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

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

          lowerCamelCase	:			Optional[Any]           =   model_labels

         lowerCamelCase	:			str           =   input_ids[index]
         lowerCamelCase	:			str           =   attention_mask[index]  # shape (seq_len,)
         lowerCamelCase	:			Union[str, Any]           =   token_type_ids[index]  # shape (seq_len, 7)
         lowerCamelCase	:			Optional[int]           =   self.model(
             input_ids=np.expand_dims(__magic_name__					,						axis=0   )					,						attention_mask=np.expand_dims(__magic_name__					,						axis=0   )					,						token_type_ids=np.expand_dims(__magic_name__					,						axis=0   )					,						)
         lowerCamelCase	:			Tuple           =   outputs.logits

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

         all_logits.append(__magic_name__   )

         lowerCamelCase	:			str           =   tfp.distributions.Bernoulli(logits=__magic_name__   )
         lowerCamelCase	:			Union[str, Any]           =   dist_per_token.probs_parameter() * tf.cast(__magic_name__					,						tf.floataa   )

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

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

        lowerCamelCase	:			str           =   tf.concat(tuple(__magic_name__   )					,						0   )

        return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__magic_name__   )					,						0   ))
      def __call__(   self					,						*__magic_name__					,						**__magic_name__   ):
       lowerCamelCase	:			Union[str, Any]           =   self._args_parser(*__magic_name__					,						**__magic_name__   )

       lowerCamelCase	:			Optional[Any]           =   super().__call__(__magic_name__					,						**__magic_name__   )
       if len(__magic_name__   ) == 1:
        return results[0]
       return results
      def 		UpperCamelCase__							(   self					,						__magic_name__=None					,						__magic_name__=None					,						__magic_name__=None					,						**__magic_name__   ):
       lowerCamelCase	:			int           =   {}
       if padding is not None:
        lowerCamelCase	:			Optional[Any]           =   padding
       if truncation is not None:
        lowerCamelCase	:			Tuple           =   truncation

       lowerCamelCase	:			List[str]           =   {}
       if sequential is not None:
        lowerCamelCase	:			Optional[int]           =   sequential
       return preprocess_params, forward_params, {}
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__=None					,						__magic_name__=True					,						__magic_name__=None   ):
       if truncation is None:
        if self.type == "tapas":
         lowerCamelCase	:			int           =   """drop_rows_to_fit"""
        else:
         lowerCamelCase	:			int           =   """do_not_truncate"""

       lowerCamelCase    ,					lowerCamelCase	:			Dict           =   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	:			Any           =   self.tokenizer(__magic_name__					,						__magic_name__					,						return_tensors=self.framework					,						truncation=__magic_name__					,						padding=__magic_name__   )
       lowerCamelCase	:			Any           =   table
       return inputs
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__=False   ):
       lowerCamelCase	:			Any           =   model_inputs.pop("""table"""   )

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






      def 		UpperCamelCase__							(   self					,						__magic_name__   ):
       lowerCamelCase	:			str           =   model_outputs["""model_inputs"""]
       lowerCamelCase	:			str           =   model_outputs["""table"""]
       lowerCamelCase	:			Any           =   model_outputs["""outputs"""]
       if self.type == "tapas":
        if self.aggregate:
         lowerCamelCase    ,					lowerCamelCase	:			Optional[int]           =   outputs[:2]
         lowerCamelCase	:			Any           =   self.tokenizer.convert_logits_to_predictions(__magic_name__					,						__magic_name__					,						__magic_name__   )
         lowerCamelCase    ,					lowerCamelCase	:			List[Any]           =   predictions
         lowerCamelCase	:			Any           =   {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__magic_name__   )}

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

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

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






from __future__ import annotations

from math import pi, pow





def 					_a      (		lowerCamelCase							):
 if side_length < 0:
  raise ValueError("""vol_cube() only accepts non-negative values"""							)
 return pow(lowerCamelCase,			3							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_spherical_cap() only accepts non-negative values"""							)
 # Volume is 1/3 pi * height squared * (3 * radius - height)
 return 1 / 3 * pi * pow(lowerCamelCase,			2							) * (3 * radius - height)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 if radius_a < 0 or radius_a < 0 or centers_distance < 0:
  raise ValueError("""vol_spheres_intersect() only accepts non-negative values"""							)
 if centers_distance == 0:
  return vol_sphere(min(lowerCamelCase,			lowerCamelCase							)							)

 lowerCamelCase	:			int           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )
 lowerCamelCase	:			Any           =   (
     (radius_a - radius_a + centers_distance)
     * (radius_a + radius_a - centers_distance)
     / (2 * centers_distance)
 )

 return vol_spherical_cap(lowerCamelCase,			lowerCamelCase							) + vol_spherical_cap(lowerCamelCase,			lowerCamelCase							)





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):

 if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
  raise ValueError(
      """vol_spheres_union() only accepts non-negative values, non-zero radius"""							)

 if centers_distance == 0:
  return vol_sphere(max(lowerCamelCase,			lowerCamelCase							)							)

 return (
     vol_sphere(lowerCamelCase							)
     + vol_sphere(lowerCamelCase							)
     - vol_spheres_intersect(lowerCamelCase,			lowerCamelCase,			lowerCamelCase							)
 )





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 if width < 0 or height < 0 or length < 0:
  raise ValueError("""vol_cuboid() only accepts non-negative values"""							)
 return float(width * height * length							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_cone() only accepts non-negative values"""							)
 return area_of_base * height / 3.0





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_right_circ_cone() only accepts non-negative values"""							)
 return pi * pow(lowerCamelCase,			2							) * height / 3.0





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_prism() only accepts non-negative values"""							)
 return float(area_of_base * height							)





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or area_of_base < 0:
  raise ValueError("""vol_pyramid() only accepts non-negative values"""							)
 return area_of_base * height / 3.0





def 					_a      (		lowerCamelCase							):
 if radius < 0:
  raise ValueError("""vol_sphere() only accepts non-negative values"""							)
 # Volume is 4/3 * pi * radius cubed
 return 4 / 3 * pi * pow(lowerCamelCase,			3							)





def 					_a      (		lowerCamelCase							):
 if radius < 0:
  raise ValueError("""vol_hemisphere() only accepts non-negative values"""							)
 # Volume is radius cubed * pi * 2/3
 return pow(lowerCamelCase,			3							) * pi * 2 / 3





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if height < 0 or radius < 0:
  raise ValueError("""vol_circular_cylinder() only accepts non-negative values"""							)
 # Volume is radius squared * height * pi
 return pow(lowerCamelCase,			2							) * height * pi





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # Volume - (outer_radius squared - inner_radius squared) * pi * height
 if inner_radius < 0 or outer_radius < 0 or height < 0:
  raise ValueError(
      """vol_hollow_circular_cylinder() only accepts non-negative values"""							)
 if outer_radius <= inner_radius:
  raise ValueError("""outer_radius must be greater than inner_radius"""							)
 return pi * (pow(lowerCamelCase,			2							) - pow(lowerCamelCase,			2							)) * height





def 					_a      (		lowerCamelCase,			lowerCamelCase,			lowerCamelCase							):
 # Volume is 1/3 * pi * height *
 #           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
 if radius_a < 0 or radius_a < 0 or height < 0:
  raise ValueError("""vol_conical_frustum() only accepts non-negative values"""							)
 return (
     1
     / 3
     * pi
     * height
     * (pow(lowerCamelCase,			2							) + pow(lowerCamelCase,			2							) + radius_a * radius_a)
 )





def 					_a      (		lowerCamelCase,			lowerCamelCase							):
 if torus_radius < 0 or tube_radius < 0:
  raise ValueError("""vol_torus() only accepts non-negative values"""							)
 return 2 * pow(lowerCamelCase,			2							) * torus_radius * pow(lowerCamelCase,			2							)





def 					_a      (		):
 print("""Volumes:"""							)
 print(F'''Cube: {vol_cube(2							) = }'''							)  # = 8
 print(F'''Cuboid: {vol_cuboid(2,			2,			2							) = }'''							)  # = 8
 print(F'''Cone: {vol_cone(2,			2							) = }'''							)  # ~= 1.33
 print(F'''Right Circular Cone: {vol_right_circ_cone(2,			2							) = }'''							)  # ~= 8.38
 print(F'''Prism: {vol_prism(2,			2							) = }'''							)  # = 4
 print(F'''Pyramid: {vol_pyramid(2,			2							) = }'''							)  # ~= 1.33
 print(F'''Sphere: {vol_sphere(2							) = }'''							)  # ~= 33.5
 print(F'''Hemisphere: {vol_hemisphere(2							) = }'''							)  # ~= 16.75
 print(F'''Circular Cylinder: {vol_circular_cylinder(2,			2							) = }'''							)  # ~= 25.1
 print(F'''Torus: {vol_torus(2,			2							) = }'''							)  # ~= 157.9
 print(F'''Conical Frustum: {vol_conical_frustum(2,			2,			4							) = }'''							)  # ~= 58.6
 print(F'''Spherical cap: {vol_spherical_cap(1,			2							) = }'''							)  # ~= 5.24
 print(F'''Spheres intersetion: {vol_spheres_intersect(2,			2,			1							) = }'''							)  # ~= 21.21
 print(F'''Spheres union: {vol_spheres_union(2,			2,			1							) = }'''							)  # ~= 45.81
 print(
     F'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,			2,			3							) = }'''							)  # ~= 28.3


if __name__ == "__main__":
      main()




