




'''simple docstring'''


import sys

from transformers import AutoTokenizer


UpperCamelCase     =				sys.argv[1]
UpperCamelCase     =				sys.argv[2]
UpperCamelCase     =				int(sys.argv[3])

UpperCamelCase     =				0

UpperCamelCase     =				AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
					for line in f_p:
										UpperCamelCase     =				line.rstrip()

										if not line:
															print(line)
															UpperCamelCase     =				0
															continue

										UpperCamelCase     =				line.split()[0]

										UpperCamelCase     =				len(tokenizer.tokenize(token))

										# Token contains strange control characters like \x96 or \x95
										# Just filter out the complete line
										if current_subwords_len == 0:
															continue

										if (subword_len_counter + current_subwords_len) > max_len:
															print('''''')
															print(line)
															UpperCamelCase     =				current_subwords_len
															continue

										subword_len_counter += current_subwords_len

										print(line)












'''simple docstring'''


import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import (
         MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )

if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow as tf
     import tensorflow_probability as tfp

     from ..models.auto.modeling_tf_auto import (
         TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
         TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
     )






class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  def __call__(   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Any=None				,     SCREAMING_SNAKE_CASE_							:       Tuple=None				,     **SCREAMING_SNAKE_CASE_							:       Optional[int]  ) ->		Optional[int]:







       '''simple docstring'''



       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(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) and table.get('''query'''  ) is not None and table.get('''table'''  ) is not None:
                 A:		List[Any]        =		[table]
            elif isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) and len(SCREAMING_SNAKE_CASE_  ) > 0:
                 if not all(isinstance(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_  ) for d in table  ):
                      raise ValueError(
                          f"""Keyword argument `table` should be a list of dict, but is {(type(SCREAMING_SNAKE_CASE_  ) for d in table)}"""  )

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

                 A:		str        =		pd.DataFrame(tqa_pipeline_input['''table''']  )

       return tqa_pipeline_inputs






@add_end_docstrings(UpperCAmelCase_       )
class 	lowerCAmelCase_  (					UpperCAmelCase_       ):







  '''simple docstring'''

  UpperCamelCase_ :			List[str]        =							"""table,query"""



  def __init__(   self							:       List[str]				,     SCREAMING_SNAKE_CASE_							:       Dict=TableQuestionAnsweringArgumentHandler()				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       str  ) ->		Dict:







       '''simple docstring'''



       super().__init__(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       A:		Tuple        =		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()  )  )

       A:		Optional[int]        =		bool(getattr(self.model.config				,     '''aggregation_labels'''				,     SCREAMING_SNAKE_CASE_  )  ) and bool(
           getattr(self.model.config				,     '''num_aggregation_labels'''				,     SCREAMING_SNAKE_CASE_  )  )
       A:		Dict        =		'''tapas''' if hasattr(self.model.config				,     '''aggregation_labels'''  ) else None



  def 				_snake_case    (   self							:       Any				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Any:







       '''simple docstring'''



       return self.model(**SCREAMING_SNAKE_CASE_  )



  def 				_snake_case    (   self							:       str				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Any:







       '''simple docstring'''



       if self.framework == "pt":
            A:		Optional[Any]        =		[]
            A:		Any        =		[]
            A:		List[str]        =		None
            A:		List[Any]        =		inputs['''input_ids'''].shape[0]

            A:		int        =		inputs['''input_ids'''].to(self.device  )
            A:		int        =		inputs['''attention_mask'''].to(self.device  )
            A:		List[str]        =		inputs['''token_type_ids'''].to(self.device  )
            A:		List[str]        =		None

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

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

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

                      A:		Optional[int]        =		torch.from_numpy(SCREAMING_SNAKE_CASE_  ).type(torch.long  ).to(self.device  )

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

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

                 all_logits.append(SCREAMING_SNAKE_CASE_  )

                 A:		Optional[Any]        =		torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_  )
                 A:		List[str]        =		dist_per_token.probs * attention_mask_example.type(torch.floataa  ).to(
                     dist_per_token.probs.device  )

                 A:		str        =		collections.defaultdict(SCREAMING_SNAKE_CASE_  )
                 for i, p in enumerate(probabilities.squeeze().tolist()  ):
                      A:		Tuple        =		token_type_ids_example[:, 0].tolist()[i]
                      A:		int        =		token_type_ids_example[:, 1].tolist()[i] - 1
                      A:		List[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(SCREAMING_SNAKE_CASE_  )

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

            A:		Union[str, Any]        =		torch.cat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  )

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

            A:		List[str]        =		inputs['''input_ids''']
            A:		Tuple        =		inputs['''attention_mask''']
            A:		Union[str, Any]        =		inputs['''token_type_ids'''].numpy()
            A:		List[Any]        =		None

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

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

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

                      A:		Optional[Any]        =		model_labels

                 A:		List[str]        =		input_ids[index]
                 A:		List[str]        =		attention_mask[index]  # shape (seq_len,)
                 A:		Dict        =		token_type_ids[index]  # shape (seq_len, 7)
                 A:		Dict        =		self.model(
                     input_ids=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     attention_mask=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     token_type_ids=np.expand_dims(SCREAMING_SNAKE_CASE_				,     axis=0  )				,     )
                 A:		List[Any]        =		outputs.logits

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

                 all_logits.append(SCREAMING_SNAKE_CASE_  )

                 A:		str        =		tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE_  )
                 A:		int        =		dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE_				,     tf.floataa  )

                 A:		Optional[Any]        =		collections.defaultdict(SCREAMING_SNAKE_CASE_  )
                 A:		Dict        =		token_type_ids_example
                 for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE_  ).numpy().tolist()  ):
                      A:		List[Any]        =		token_type_ids_example[:, 0].tolist()[i]
                      A:		Optional[Any]        =		token_type_ids_example[:, 1].tolist()[i] - 1
                      A:		Optional[int]        =		token_type_ids_example[:, 2].tolist()[i] - 1
                      if col >= 0 and row >= 0 and segment_id == 1:
                           coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE_  )

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

            A:		int        =		tf.concat(tuple(SCREAMING_SNAKE_CASE_  )				,     0  )

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



  def __call__(   self							:       Any				,     *SCREAMING_SNAKE_CASE_							:       Any				,     **SCREAMING_SNAKE_CASE_							:       Union[str, Any]  ) ->		str:







       '''simple docstring'''



       A:		List[str]        =		self._args_parser(*SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )

       A:		Optional[Any]        =		super().__call__(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_  )
       if len(SCREAMING_SNAKE_CASE_  ) == 1:
            return results[0]
       return results



  def 				_snake_case    (   self							:       int				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       Optional[Any]=None				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=None				,     **SCREAMING_SNAKE_CASE_							:       List[Any]  ) ->		Optional[Any]:







       '''simple docstring'''



       A:		Tuple        =		{}
       if padding is not None:
            A:		Union[str, Any]        =		padding
       if truncation is not None:
            A:		Dict        =		truncation

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



  def 				_snake_case    (   self							:       str				,     SCREAMING_SNAKE_CASE_							:       Any				,     SCREAMING_SNAKE_CASE_							:       Optional[int]=None				,     SCREAMING_SNAKE_CASE_							:       List[Any]=True				,     SCREAMING_SNAKE_CASE_							:       int=None  ) ->		str:







       '''simple docstring'''



       if truncation is None:
            if self.type == "tapas":
                 A:		Optional[Any]        =		'''drop_rows_to_fit'''
            else:
                 A:		str        =		'''do_not_truncate'''

       A    ,						A:		List[Any]        =		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'''  )
       A:		str        =		self.tokenizer(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_				,     return_tensors=self.framework				,     truncation=SCREAMING_SNAKE_CASE_				,     padding=SCREAMING_SNAKE_CASE_  )
       A:		Optional[Any]        =		table
       return inputs



  def 				_snake_case    (   self							:       Optional[Any]				,     SCREAMING_SNAKE_CASE_							:       int				,     SCREAMING_SNAKE_CASE_							:       Union[str, Any]=False  ) ->		Tuple:







       '''simple docstring'''



       A:		Optional[int]        =		model_inputs.pop('''table'''  )

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




  def 				_snake_case    (   self							:       Optional[int]				,     SCREAMING_SNAKE_CASE_							:       Optional[int]  ) ->		Dict:







       '''simple docstring'''



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

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

                 answers.append(SCREAMING_SNAKE_CASE_  )
            if len(SCREAMING_SNAKE_CASE_  ) == 0:
                 raise PipelineException('''Empty answer'''  )
       else:
            A:		int        =		[{'''answer''': answer} for answer in self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_				,     skip_special_tokens=SCREAMING_SNAKE_CASE_  )]

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






