




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__			(lowerCAmelCase__  ):

    '''simple docstring'''



    def __call__(					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   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(UpperCamelCase__  ,						UpperCamelCase__  ) and table.get("query"  ) is not None and table.get("table"  ) is not None:
                         lowerCamelCase     :   Optional[int]    				=      [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:
                                lowerCamelCase     :   int    				=      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:
                  lowerCamelCase     :   Tuple    				=      [{"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     :   Optional[Any]    				=      pd.DataFrame(tqa_pipeline_input["table"]  )

           return tqa_pipeline_inputs




@add_end_docstrings(lowerCAmelCase__  )
class 	UpperCamelCase__			(lowerCAmelCase__  ):

    '''simple docstring'''



    lowerCamelCase_   :			Optional[Any]			  =   """table,query"""







    def __init__(					self  ,						UpperCamelCase__=TableQuestionAnsweringArgumentHandler()  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Dict:
           super().__init__(*UpperCamelCase__  ,						**UpperCamelCase__  )
           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     :   Any    				=      bool(getattr(self.model.config  ,						"aggregation_labels"  ,						UpperCamelCase__  )  ) and bool(
               getattr(self.model.config  ,						"num_aggregation_labels"  ,						UpperCamelCase__  )  )
           lowerCamelCase     :   List[Any]    				=      "tapas" if hasattr(self.model.config  ,						"aggregation_labels"  ) else None







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   int:
           return self.model(**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						**UpperCamelCase__  )  ->   Union[str, Any]:
           if self.framework == "pt":
                  lowerCamelCase     :   List[str]    				=      []
                  lowerCamelCase     :   List[Any]    				=      []
                  lowerCamelCase     :   List[Any]    				=      None
                  lowerCamelCase     :   List[str]    				=      inputs["input_ids"].shape[0]

                  lowerCamelCase     :   Optional[int]    				=      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     :   str    				=      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:
                                lowerCamelCase     :   List[Any]    				=      token_type_ids_example[:, 3]  # shape (seq_len,)
                                lowerCamelCase     :   Optional[int]    				=      np.zeros_like(prev_labels_example.cpu().numpy()  )  # shape (seq_len,)

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

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

                                lowerCamelCase     :   Dict    				=      torch.from_numpy(UpperCamelCase__  ).type(torch.long  ).to(self.device  )

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

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

                         all_logits.append(UpperCamelCase__  )

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

                         lowerCamelCase     :   Optional[int]    				=      collections.defaultdict(UpperCamelCase__  )
                         for i, p in enumerate(probabilities.squeeze().tolist()  ):
                                lowerCamelCase     :   Optional[int]    				=      token_type_ids_example[:, 0].tolist()[i]
                                lowerCamelCase     :   List[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(UpperCamelCase__  )

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

                  lowerCamelCase     :   Optional[Any]    				=      torch.cat(tuple(UpperCamelCase__  )  ,						0  )

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

                  lowerCamelCase     :   Dict    				=      inputs["input_ids"]
                  lowerCamelCase     :   Tuple    				=      inputs["attention_mask"]
                  lowerCamelCase     :   Optional[int]    				=      inputs["token_type_ids"].numpy()
                  lowerCamelCase     :   Union[str, Any]    				=      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:
                                lowerCamelCase     :   Tuple    				=      token_type_ids_example[:, 3]  # shape (seq_len,)
                                lowerCamelCase     :   Optional[Any]    				=      np.zeros_like(UpperCamelCase__  ,						dtype=np.intaa  )  # shape (seq_len,)

                                lowerCamelCase     :   Optional[Any]    				=      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     :   int    				=      token_type_ids_example[:, 1].tolist()[i] - 1
                                       lowerCamelCase     :   Union[str, Any]    				=      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     :   Any    				=      model_labels

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

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

                         all_logits.append(UpperCamelCase__  )

                         lowerCamelCase     :   Optional[int]    				=      tfp.distributions.Bernoulli(logits=UpperCamelCase__  )
                         lowerCamelCase     :   Any    				=      dist_per_token.probs_parameter() * tf.cast(UpperCamelCase__  ,						tf.floataa  )

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

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

                  lowerCamelCase     :   int    				=      tf.concat(tuple(UpperCamelCase__  )  ,						0  )

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







    def __call__(					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   int:
           lowerCamelCase     :   Union[str, Any]    				=      self._args_parser(*UpperCamelCase__  ,						**UpperCamelCase__  )

           lowerCamelCase     :   Optional[int]    				=      super().__call__(UpperCamelCase__  ,						**UpperCamelCase__  )
           if len(UpperCamelCase__  ) == 1:
                  return results[0]
           return results







    def 					_lowercase  (					self  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						UpperCamelCase__=None  ,						**UpperCamelCase__  )  ->   Optional[int]:
           lowerCamelCase     :   int    				=      {}
           if padding is not None:
                  lowerCamelCase     :   Dict    				=      padding
           if truncation is not None:
                  lowerCamelCase     :   Tuple    				=      truncation

           lowerCamelCase     :   List[str]    				=      {}
           if sequential is not None:
                  lowerCamelCase     :   Any    				=      sequential
           return preprocess_params, forward_params, {}







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=None  ,						UpperCamelCase__=True  ,						UpperCamelCase__=None  )  ->   str:
           if truncation is None:
                  if self.type == "tapas":
                         lowerCamelCase     :   Optional[Any]    				=      "drop_rows_to_fit"
                  else:
                         lowerCamelCase     :   str    				=      "do_not_truncate"

           lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      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(UpperCamelCase__  ,						UpperCamelCase__  ,						return_tensors=self.framework  ,						truncation=UpperCamelCase__  ,						padding=UpperCamelCase__  )
           lowerCamelCase     :   str    				=      table
           return inputs







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__=False  )  ->   Dict:
           lowerCamelCase     :   Union[str, Any]    				=      model_inputs.pop("table"  )

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







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

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

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

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












import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union




class 	UpperCamelCase__			(enum.Enum  ):

    '''simple docstring'''



    lowerCamelCase_   :			Tuple			  =   """timm"""
    lowerCamelCase_   :			Union[str, Any]			  =   """transformers"""




def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Optional[int]:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"					)

       if out_features is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list,)					):
                     raise ValueError(f'''out_features must be a list {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(feat not in stage_names for feat in out_features					):
                     raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''					)

       if out_indices is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list, tuple)					):
                     raise ValueError(f'''out_indices must be a list or tuple, got {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(idx >= len(_SCREAMING_SNAKE_CASE					) for idx in out_indices					):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

       if out_features is not None and out_indices is not None:
              if len(_SCREAMING_SNAKE_CASE					) != len(_SCREAMING_SNAKE_CASE					):
                     raise ValueError("out_features and out_indices should have the same length if both are set"					)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Optional[Any]:
       if out_indices is None and out_features is None:
              lowerCamelCase     :   Dict    				=      [len(_SCREAMING_SNAKE_CASE					) - 1]
              lowerCamelCase     :   Tuple    				=      [stage_names[-1]]
       elif out_indices is None and out_features is not None:
              lowerCamelCase     :   List[Any]    				=      [stage_names.index(_SCREAMING_SNAKE_CASE					) for layer in out_features]
       elif out_features is None and out_indices is not None:
              lowerCamelCase     :   Union[str, Any]    				=      [stage_names[idx] for idx in out_indices]
       return out_features, out_indices

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase				,   lowerCamelCase     :   str    				=      _align_output_features_output_indices(
           out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       return output_features, output_indices




class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			Optional[BackboneType]			  =   None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           if getattr(self  ,						"_backbone"  ,						UpperCamelCase__  ) is None:
                  raise ValueError("self._backbone must be set before calling _init_timm_backbone"  )

           # These will diagree with the defaults for the transformers models e.g. for resnet50
           # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
           # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
           lowerCamelCase     :   Any    				=      [stage["module"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   Optional[int]    				=      [stage["num_chs"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   str    				=      self._backbone.feature_info.out_indices
           lowerCamelCase     :   Any    				=      self._backbone.feature_info.module_name()

           # We verify the out indices and out features are valid
           verify_out_features_out_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )
           lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      out_features, out_indices







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"stage_names"  )
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"out_features"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      getattr(UpperCamelCase__  ,						"out_indices"  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[Any]    				=      stage_names
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=UpperCamelCase__  )
           # Number of channels for each stage. This is set in the transformer backbone model init
           lowerCamelCase     :   Tuple    				=      None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Any    				=      config

           lowerCamelCase     :   Any    				=      getattr(UpperCamelCase__  ,						"use_timm_backbone"  ,						UpperCamelCase__  )
           lowerCamelCase     :   int    				=      BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

           if self.backbone_type == BackboneType.TIMM:
                  self._init_timm_backbone(UpperCamelCase__  )
           elif self.backbone_type == BackboneType.TRANSFORMERS:
                  self._init_transformers_backbone(UpperCamelCase__  )
           else:
                  raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   List[str]:
           lowerCamelCase				,   lowerCamelCase     :   int    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           # the current backbones will output the number of channels for each stage
           # even if that stage is not in the out_features list.
           return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return [self.out_feature_channels[name] for name in self.out_features]







    def 					_lowercase  (					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Union[str, Any]    				=      dict(inspect.signature(self.forward  ).parameters  )
           lowerCamelCase     :   Optional[int]    				=      {k: v for k, v in kwargs.items() if k in signature}
           return self(*UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						)  ->   int:
           raise NotImplementedError("This method should be implemented by the derived class."  )







    def 					_lowercase  (					self  )  ->   Dict:
           lowerCamelCase     :   List[Any]    				=      super().to_dict()
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Dict    				=      output.pop("_out_indices"  )
           return output




class 	UpperCamelCase__			:

    '''simple docstring'''



    @property
    def 					_lowercase  (					self  )  ->   int:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Optional[Any]:
           lowerCamelCase				,   lowerCamelCase     :   List[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   int:
           lowerCamelCase				,   lowerCamelCase     :   Optional[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   str    				=      super().to_dict()
           lowerCamelCase     :   Union[str, Any]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_indices"  )
           return output






