




"""simple docstring"""




import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor



def   lowerCAmelCase						(						_lowerCAmelCase							:				Tuple		):




 """simple docstring"""







 UpperCAmelCase__								=  TimesformerConfig()

 if "large" in model_name:
  UpperCAmelCase__								=  96

 if "hr" in model_name:
  UpperCAmelCase__								=  16
  UpperCAmelCase__								=  448

 UpperCAmelCase__								=  "huggingface/label-files"
 if "k400" in model_name:
  UpperCAmelCase__								=  400
  UpperCAmelCase__								=  "kinetics400-id2label.json"
 elif "k600" in model_name:
  UpperCAmelCase__								=  600
  UpperCAmelCase__								=  "kinetics600-id2label.json"
 elif "ssv2" in model_name:
  UpperCAmelCase__								=  174
  UpperCAmelCase__								=  "something-something-v2-id2label.json"
 else:
  raise ValueError("Model name should either contain 'k400', 'k600' or 'ssv2'."		)
 UpperCAmelCase__								=  json.load(open(hf_hub_download(_lowerCAmelCase  ,							_lowerCAmelCase  ,							repo_type="dataset"		)  ,							"r"		)		)
 UpperCAmelCase__								=  {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
 UpperCAmelCase__								=  idalabel
 UpperCAmelCase__								=  {v: k for k, v in idalabel.items()}

 return config



def   lowerCAmelCase						(						_lowerCAmelCase							:				Any		):




 """simple docstring"""







 if "encoder." in name:
  UpperCAmelCase__								=  name.replace("encoder."  ,							""		)
 if "cls_token" in name:
  UpperCAmelCase__								=  name.replace("cls_token"  ,							"timesformer.embeddings.cls_token"		)
 if "pos_embed" in name:
  UpperCAmelCase__								=  name.replace("pos_embed"  ,							"timesformer.embeddings.position_embeddings"		)
 if "time_embed" in name:
  UpperCAmelCase__								=  name.replace("time_embed"  ,							"timesformer.embeddings.time_embeddings"		)
 if "patch_embed.proj" in name:
  UpperCAmelCase__								=  name.replace("patch_embed.proj"  ,							"timesformer.embeddings.patch_embeddings.projection"		)
 if "patch_embed.norm" in name:
  UpperCAmelCase__								=  name.replace("patch_embed.norm"  ,							"timesformer.embeddings.norm"		)
 if "blocks" in name:
  UpperCAmelCase__								=  name.replace("blocks"  ,							"timesformer.encoder.layer"		)
 if "attn.proj" in name:
  UpperCAmelCase__								=  name.replace("attn.proj"  ,							"attention.output.dense"		)
 if "attn" in name and "bias" not in name and "temporal" not in name:
  UpperCAmelCase__								=  name.replace("attn"  ,							"attention.self"		)
 if "attn" in name and "temporal" not in name:
  UpperCAmelCase__								=  name.replace("attn"  ,							"attention.attention"		)
 if "temporal_norm1" in name:
  UpperCAmelCase__								=  name.replace("temporal_norm1"  ,							"temporal_layernorm"		)
 if "temporal_attn.proj" in name:
  UpperCAmelCase__								=  name.replace("temporal_attn"  ,							"temporal_attention.output.dense"		)
 if "temporal_fc" in name:
  UpperCAmelCase__								=  name.replace("temporal_fc"  ,							"temporal_dense"		)
 if "norm1" in name and "temporal" not in name:
  UpperCAmelCase__								=  name.replace("norm1"  ,							"layernorm_before"		)
 if "norm2" in name:
  UpperCAmelCase__								=  name.replace("norm2"  ,							"layernorm_after"		)
 if "mlp.fc1" in name:
  UpperCAmelCase__								=  name.replace("mlp.fc1"  ,							"intermediate.dense"		)
 if "mlp.fc2" in name:
  UpperCAmelCase__								=  name.replace("mlp.fc2"  ,							"output.dense"		)
 if "norm.weight" in name and "fc" not in name and "temporal" not in name:
  UpperCAmelCase__								=  name.replace("norm.weight"  ,							"timesformer.layernorm.weight"		)
 if "norm.bias" in name and "fc" not in name and "temporal" not in name:
  UpperCAmelCase__								=  name.replace("norm.bias"  ,							"timesformer.layernorm.bias"		)
 if "head" in name:
  UpperCAmelCase__								=  name.replace("head"  ,							"classifier"		)

 return name



def   lowerCAmelCase						(						_lowerCAmelCase							:				Union[str, Any]  ,							_lowerCAmelCase							:				List[Any]		):




 """simple docstring"""







 for key in orig_state_dict.copy().keys():
  UpperCAmelCase__								=  orig_state_dict.pop(_lowerCAmelCase		)

  if key.startswith("model."		):
   UpperCAmelCase__								=  key.replace("model."  ,							""		)

  if "qkv" in key:
   UpperCAmelCase__								=  key.split("."		)
   UpperCAmelCase__								=  int(key_split[1]		)
   UpperCAmelCase__								=  "timesformer.encoder.layer."
   if "temporal" in key:
    UpperCAmelCase__								=  ".temporal_attention.attention.qkv."
   else:
    UpperCAmelCase__								=  ".attention.attention.qkv."
   if "weight" in key:
    UpperCAmelCase__								=  val
   else:
    UpperCAmelCase__								=  val
  else:
   UpperCAmelCase__								=  val

 return orig_state_dict



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"  ,							filename="eating_spaghetti.npy"  ,							repo_type="dataset"		)
 UpperCAmelCase__								=  np.load(_lowerCAmelCase		)
 return list(_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				Tuple  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 UpperCAmelCase__								=  get_timesformer_config(_lowerCAmelCase		)

 UpperCAmelCase__								=  TimesformerForVideoClassification(_lowerCAmelCase		)

 # download original checkpoint, hosted on Google Drive
 UpperCAmelCase__								=  "pytorch_model.bin"
 gdown.cached_download(_lowerCAmelCase  ,							_lowerCAmelCase  ,							quiet=_lowerCAmelCase		)
 UpperCAmelCase__								=  torch.load(_lowerCAmelCase  ,							map_location="cpu"		)
 if "model" in files:
  UpperCAmelCase__								=  files["model"]
 elif "module" in files:
  UpperCAmelCase__								=  files["module"]
 else:
  UpperCAmelCase__								=  files["model_state"]
 UpperCAmelCase__								=  convert_state_dict(_lowerCAmelCase  ,							_lowerCAmelCase		)

 model.load_state_dict(_lowerCAmelCase		)
 model.eval()

 # verify model on basic input
 UpperCAmelCase__								=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,							image_std=[0.5, 0.5, 0.5]		)
 UpperCAmelCase__								=  prepare_video()
 UpperCAmelCase__								=  image_processor(video[:8]  ,							return_tensors="pt"		)

 UpperCAmelCase__								=  model(**_lowerCAmelCase		)
 UpperCAmelCase__								=  outputs.logits

 UpperCAmelCase__								=  [
     # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k400",
     "timesformer-large-finetuned-k400",
     "timesformer-hr-finetuned-k400",
     # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-k600",
     "timesformer-large-finetuned-k600",
     "timesformer-hr-finetuned-k600",
     # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
     "timesformer-base-finetuned-ssv2",
     "timesformer-large-finetuned-ssv2",
     "timesformer-hr-finetuned-ssv2",
 ]

 # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
 if model_name == "timesformer-base-finetuned-k400":
  UpperCAmelCase__								=  torch.Size([1, 400]		)
  UpperCAmelCase__								=  torch.tensor([-0.3_016, -0.7_713, -0.4_205]		)
 elif model_name == "timesformer-base-finetuned-k600":
  UpperCAmelCase__								=  torch.Size([1, 600]		)
  UpperCAmelCase__								=  torch.tensor([-0.7_267, -0.7_466, 3.2_404]		)
 elif model_name == "timesformer-base-finetuned-ssv2":
  UpperCAmelCase__								=  torch.Size([1, 174]		)
  UpperCAmelCase__								=  torch.tensor([-0.9_059, 0.6_433, -3.1_457]		)
 elif model_name == "timesformer-large-finetuned-k400":
  UpperCAmelCase__								=  torch.Size([1, 400]		)
  UpperCAmelCase__								=  torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-large-finetuned-k600":
  UpperCAmelCase__								=  torch.Size([1, 600]		)
  UpperCAmelCase__								=  torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-large-finetuned-ssv2":
  UpperCAmelCase__								=  torch.Size([1, 174]		)
  UpperCAmelCase__								=  torch.tensor([0, 0, 0]		)
 elif model_name == "timesformer-hr-finetuned-k400":
  UpperCAmelCase__								=  torch.Size([1, 400]		)
  UpperCAmelCase__								=  torch.tensor([-0.9_617, -3.7_311, -3.7_708]		)
 elif model_name == "timesformer-hr-finetuned-k600":
  UpperCAmelCase__								=  torch.Size([1, 600]		)
  UpperCAmelCase__								=  torch.tensor([2.5_273, 0.7_127, 1.8_848]		)
 elif model_name == "timesformer-hr-finetuned-ssv2":
  UpperCAmelCase__								=  torch.Size([1, 174]		)
  UpperCAmelCase__								=  torch.tensor([-3.6_756, -0.7_513, 0.7_180]		)
 else:
  raise ValueError(F'''Model name not supported. Should be one of {model_names}'''		)

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]  ,							_lowerCAmelCase  ,							atol=1E-4		)
 print("Logits ok!"		)

 if pytorch_dump_folder_path is not None:
  print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''		)
  image_processor.save_pretrained(_lowerCAmelCase		)
  model.save_pretrained(_lowerCAmelCase		)

 if push_to_hub:
  print("Pushing to the hub..."		)
  model.push_to_hub(F'''fcakyon/{model_name}'''		)


if __name__ == "__main__":
  _lowerCAmelCase  :				int						=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download",
      type=str,
      help=(
          "URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct"
          " download link."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument("--model_name", default="timesformer-base-finetuned-k400", type=str, help="Name of the model.")
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  _lowerCAmelCase  :				str						=				parser.parse_args()
  convert_timesformer_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
  )











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

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

         UpperCAmelCase__								=  pd.DataFrame(tqa_pipeline_input["table"] )

       return tqa_pipeline_inputs



@add_end_docstrings(lowerCAmelCase						)
class      _UpperCamelCase (			lowerCAmelCase						):
      UpperCAmelCase_            =						"""table,query"""
      def __init__(     self				:int	, lowerCamelCase				:Any=TableQuestionAnsweringArgumentHandler()	, *lowerCamelCase				:Optional[int]	, **lowerCamelCase				:str )       ->     int:
       super().__init__(*lowerCamelCase	, **lowerCamelCase )
       UpperCAmelCase__								=  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() ) )

       UpperCAmelCase__								=  bool(getattr(self.model.config	, "aggregation_labels"	, lowerCamelCase ) ) and bool(
           getattr(self.model.config	, "num_aggregation_labels"	, lowerCamelCase ) )
       UpperCAmelCase__								=  "tapas" if hasattr(self.model.config	, "aggregation_labels" ) else None
      def 		UpperCAmelCase_  (     self				:int	, **lowerCamelCase				:Dict )       ->     Optional[int]:
       return self.model(**lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Dict	, **lowerCamelCase				:Optional[int] )       ->     Dict:
       if self.framework == "pt":
        UpperCAmelCase__								=  []
        UpperCAmelCase__								=  []
        UpperCAmelCase__								=  None
        UpperCAmelCase__								=  inputs["input_ids"].shape[0]

        UpperCAmelCase__								=  inputs["input_ids"].to(self.device )
        UpperCAmelCase__								=  inputs["attention_mask"].to(self.device )
        UpperCAmelCase__								=  inputs["token_type_ids"].to(self.device )
        UpperCAmelCase__								=  None

        for index in range(lowerCamelCase ):
         # If sequences have already been processed, the token type IDs will be created according to the previous
         # answer.
         if prev_answers is not None:
          UpperCAmelCase__								=  token_type_ids_example[:, 3]  # shape (seq_len,)
          UpperCAmelCase__								=  np.zeros_like(prev_labels_example.cpu().numpy() )  # shape (seq_len,)

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

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

          UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase ).type(torch.long ).to(self.device )

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

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

         all_logits.append(lowerCamelCase )

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

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

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

        UpperCAmelCase__								=  torch.cat(tuple(lowerCamelCase )	, 0 )

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

        UpperCAmelCase__								=  inputs["input_ids"]
        UpperCAmelCase__								=  inputs["attention_mask"]
        UpperCAmelCase__								=  inputs["token_type_ids"].numpy()
        UpperCAmelCase__								=  None

        for index in range(lowerCamelCase ):
         # If sequences have already been processed, the token type IDs will be created according to the previous
         # answer.
         if prev_answers is not None:
          UpperCAmelCase__								=  token_type_ids_example[:, 3]  # shape (seq_len,)
          UpperCAmelCase__								=  np.zeros_like(lowerCamelCase	, dtype=np.intaa )  # shape (seq_len,)

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

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

          UpperCAmelCase__								=  model_labels

         UpperCAmelCase__								=  input_ids[index]
         UpperCAmelCase__								=  attention_mask[index]  # shape (seq_len,)
         UpperCAmelCase__								=  token_type_ids[index]  # shape (seq_len, 7)
         UpperCAmelCase__								=  self.model(
             input_ids=np.expand_dims(lowerCamelCase	, axis=0 )	, attention_mask=np.expand_dims(lowerCamelCase	, axis=0 )	, token_type_ids=np.expand_dims(lowerCamelCase	, axis=0 )	, )
         UpperCAmelCase__								=  outputs.logits

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

         all_logits.append(lowerCamelCase )

         UpperCAmelCase__								=  tfp.distributions.Bernoulli(logits=lowerCamelCase )
         UpperCAmelCase__								=  dist_per_token.probs_parameter() * tf.cast(lowerCamelCase	, tf.floataa )

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

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

        UpperCAmelCase__								=  tf.concat(tuple(lowerCamelCase )	, 0 )

        return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowerCamelCase )	, 0 ))
      def __call__(     self				:str	, *lowerCamelCase				:List[Any]	, **lowerCamelCase				:List[Any] )       ->     Dict:
       UpperCAmelCase__								=  self._args_parser(*lowerCamelCase	, **lowerCamelCase )

       UpperCAmelCase__								=  super().__call__(lowerCamelCase	, **lowerCamelCase )
       if len(lowerCamelCase ) == 1:
        return results[0]
       return results
      def 		UpperCAmelCase_  (     self				:List[str]	, lowerCamelCase				:int=None	, lowerCamelCase				:Optional[int]=None	, lowerCamelCase				:int=None	, **lowerCamelCase				:Tuple )       ->     Tuple:
       UpperCAmelCase__								=  {}
       if padding is not None:
        UpperCAmelCase__								=  padding
       if truncation is not None:
        UpperCAmelCase__								=  truncation

       UpperCAmelCase__								=  {}
       if sequential is not None:
        UpperCAmelCase__								=  sequential
       return preprocess_params, forward_params, {}
      def 		UpperCAmelCase_  (     self				:Optional[int]	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Tuple=None	, lowerCamelCase				:Union[str, Any]=True	, lowerCamelCase				:str=None )       ->     Union[str, Any]:
       if truncation is None:
        if self.type == "tapas":
         UpperCAmelCase__								=  "drop_rows_to_fit"
        else:
         UpperCAmelCase__								=  "do_not_truncate"

       UpperCAmelCase__	,	UpperCAmelCase__								=  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" )
       UpperCAmelCase__								=  self.tokenizer(lowerCamelCase	, lowerCamelCase	, return_tensors=self.framework	, truncation=lowerCamelCase	, padding=lowerCamelCase )
       UpperCAmelCase__								=  table
       return inputs
      def 		UpperCAmelCase_  (     self				:Union[str, Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:Optional[Any]=False )       ->     int:
       UpperCAmelCase__								=  model_inputs.pop("table" )

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







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

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

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

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





