


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 	_snake_case						(   _snake_case							):




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						**_lowerCamelCase					):
       # 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:
                   a		:Union[str, Any]											= [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:
                         a		: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(_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:
             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		:List[str]											= pd.DataFrame(tqa_pipeline_input['''table''']					)

       return tqa_pipeline_inputs







@add_end_docstrings(_snake_case							)
class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'table,query'




 def __init__(	self      ,						_lowerCamelCase=TableQuestionAnsweringArgumentHandler()      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(*_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:Optional[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()					)					)

       a		:Union[str, Any]											= bool(getattr(self.model.config      ,						'''aggregation_labels'''      ,						_lowerCamelCase					)					) and bool(
           getattr(self.model.config      ,						'''num_aggregation_labels'''      ,						_lowerCamelCase					)					)
       a		:Union[str, Any]											= '''tapas''' if hasattr(self.model.config      ,						'''aggregation_labels'''					) else None




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       return self.model(**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       if self.framework == "pt":
             a		:List[str]											= []
             a		:List[str]											= []
             a		:Tuple											= None
             a		:Optional[int]											= inputs['''input_ids'''].shape[0]

             a		:Any											= inputs['''input_ids'''].to(self.device					)
             a		:List[Any]											= inputs['''attention_mask'''].to(self.device					)
             a		:Tuple											= inputs['''token_type_ids'''].to(self.device					)
             a		:List[Any]											= 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:
                         a		:Optional[int]											= token_type_ids_example[:, 3]  # shape (seq_len,)
                         a		:str											= np.zeros_like(prev_labels_example.cpu().numpy()					)  # shape (seq_len,)

                         a		:str											= token_type_ids[index]  # shape (seq_len, 7)
                         for i in range(model_labels.shape[0]					):
                               a		:List[str]											= token_type_ids_example[:, 0].tolist()[i]
                               a		:Union[str, Any]											= token_type_ids_example[:, 1].tolist()[i] - 1
                               a		:List[Any]											= 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		:List[str]											= torch.from_numpy(_lowerCamelCase					).type(torch.long					).to(self.device					)

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

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

                   all_logits.append(_lowerCamelCase					)

                   a		:Any											= torch.distributions.Bernoulli(logits=_lowerCamelCase					)
                   a		:int											= dist_per_token.probs * attention_mask_example.type(torch.floataa					).to(
                       dist_per_token.probs.device					)

                   a		:Dict											= collections.defaultdict(_lowerCamelCase					)
                   for i, p in enumerate(probabilities.squeeze().tolist()					):
                         a		:Dict											= token_type_ids_example[:, 0].tolist()[i]
                         a		:Optional[Any]											= token_type_ids_example[:, 1].tolist()[i] - 1
                         a		:Dict											= 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					)

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

             a		:Union[str, Any]											= torch.cat(tuple(_lowerCamelCase					)      ,						0					)

             return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase					)      ,						0					))
       else:
             a		:Dict											= []
             a		:Any											= []
             a		:Optional[int]											= None
             a		:Tuple											= inputs['''input_ids'''].shape[0]

             a		:Tuple											= inputs['''input_ids''']
             a		:str											= inputs['''attention_mask''']
             a		:Tuple											= inputs['''token_type_ids'''].numpy()
             a		:Any											= 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:
                         a		:Optional[int]											= token_type_ids_example[:, 3]  # shape (seq_len,)
                         a		:List[str]											= np.zeros_like(_lowerCamelCase      ,						dtype=np.intaa					)  # shape (seq_len,)

                         a		:Union[str, Any]											= 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		:Optional[int]											= token_type_ids_example[:, 1].tolist()[i] - 1
                               a		:List[Any]											= token_type_ids_example[:, 2].tolist()[i] - 1

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

                         a		:Optional[Any]											= model_labels

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

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

                   all_logits.append(_lowerCamelCase					)

                   a		:Any											= tfp.distributions.Bernoulli(logits=_lowerCamelCase					)
                   a		:int											= dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase      ,						tf.floataa					)

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

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

             a		:str											= tf.concat(tuple(_lowerCamelCase					)      ,						0					)

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




 def __call__(	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       a		:Tuple											= self._args_parser(*_lowerCamelCase      ,						**_lowerCamelCase					)

       a		:int											= super().__call__(_lowerCamelCase      ,						**_lowerCamelCase					)
       if len(_lowerCamelCase					) == 1:
             return results[0]
       return results




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						**_lowerCamelCase					):
       a		:Optional[int]											= {}
       if padding is not None:
             a		:List[Any]											= padding
       if truncation is not None:
             a		:Tuple											= truncation

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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=None      ,						_lowerCamelCase=True      ,						_lowerCamelCase=None					):
       if truncation is None:
             if self.type == "tapas":
                   a		:List[Any]											= '''drop_rows_to_fit'''
             else:
                   a		:List[Any]											= '''do_not_truncate'''

       a							,       a		:Optional[int]											= 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		:Any											= self.tokenizer(_lowerCamelCase      ,						_lowerCamelCase      ,						return_tensors=self.framework      ,						truncation=_lowerCamelCase      ,						padding=_lowerCamelCase					)
       a		:Tuple											= table
       return inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=False					):
       a		:List[str]											= model_inputs.pop('''table'''					)

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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:Optional[Any]											= model_outputs['''model_inputs''']
       a		:int											= model_outputs['''table''']
       a		:Optional[int]											= model_outputs['''outputs''']
       if self.type == "tapas":
             if self.aggregate:
                   a							,       a		:Tuple											= outputs[:2]
                   a		:Optional[int]											= self.tokenizer.convert_logits_to_predictions(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
                   a							,       a		:int											= predictions
                   a		:Optional[Any]											= {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase					)}

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

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

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









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'semantic'




 def __init__(	self      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=True      ,						_lowerCamelCase=768      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_0048      ,						_lowerCamelCase=12_9595      ,						_lowerCamelCase=12_9599      ,						_lowerCamelCase=1_0000      ,						_lowerCamelCase=256      ,						_lowerCamelCase=49.9      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						eos_token_id=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						max_new_tokens=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'coarse_acoustics'




 def __init__(	self      ,						_lowerCamelCase=True      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=False      ,						_lowerCamelCase=0.7      ,						_lowerCamelCase=True      ,						_lowerCamelCase=1_2048      ,						_lowerCamelCase=75      ,						_lowerCamelCase=2      ,						_lowerCamelCase=1_2050      ,						_lowerCamelCase=256      ,						_lowerCamelCase = 630      ,						_lowerCamelCase = 60      ,						**_lowerCamelCase      ,						):
       super().__init__(
           temperature=_lowerCamelCase      ,						do_sample=_lowerCamelCase      ,						renormalize_logits=_lowerCamelCase      ,						output_scores=_lowerCamelCase      ,						return_dict_in_generate=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						output_attentions=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       if semantic_config is None:
             a		:str											= {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''					)

       if coarse_acoustics_config is None:
             a		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

       if fine_acoustics_config is None:
             a		:Any											= {}
             logger.info('''fine_acoustics_config is None. initializing the fine model with default values.'''					)

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       return cls(
           semantic_config=semantic_config.to_dict()      ,						coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,						fine_acoustics_config=fine_acoustics_config.to_dict()      ,						**_lowerCamelCase      ,						)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

       a		:List[Any]											= self.semantic_config.to_dict()
       a		:Union[str, Any]											= self.coarse_acoustics_config.to_dict()
       a		:int											= self.fine_acoustics_config.to_dict()

       a		:Tuple											= self.__class__.model_type
       return output





