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(    A__				):






 """simple docstring"""




 def __call__(    self ,       __magic_name__=None ,       __magic_name__=None ,       **__magic_name__      ):

        """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(lowerCAmelCase_ ,       lowerCAmelCase_      ) and table.get('''query'''      ) is not None and table.get('''table'''      ) is not None:
                      A_							:       Optional[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_							:       Optional[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_							:       Dict	      =				[{'''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_							:       Tuple	      =				pd.DataFrame(tqa_pipeline_input['''table''']      )

        return tqa_pipeline_inputs






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   """table,query"""
 def __init__(    self ,       __magic_name__=TableQuestionAnsweringArgumentHandler() ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*lowerCAmelCase_ ,       **lowerCAmelCase_      )
        A_							:       Dict	      =				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_							:       Dict	      =				bool(getattr(self.model.config ,       '''aggregation_labels''' ,       lowerCAmelCase_      )      ) and bool(
            getattr(self.model.config ,       '''num_aggregation_labels''' ,       lowerCAmelCase_      )      )
        A_							:       Tuple	      =				'''tapas''' if hasattr(self.model.config ,       '''aggregation_labels'''      ) else None
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return self.model(**lowerCAmelCase_      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

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

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

                             A_							:       Optional[Any]	      =				token_type_ids[index]  # shape (seq_len, 7)
                             for i in range(model_labels.shape[0]      ):
                                    A_							:       List[Any]	      =				token_type_ids_example[:, 0].tolist()[i]
                                    A_							:       List[Any]	      =				token_type_ids_example[:, 1].tolist()[i] - 1
                                    A_							:       Optional[int]	      =				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_							:       int	      =				torch.from_numpy(lowerCAmelCase_      ).type(torch.long      ).to(self.device      )

                      A_							:       List[str]	      =				input_ids[index]
                      A_							:       Any	      =				attention_mask[index]  # shape (seq_len,)
                      A_							:       Optional[Any]	      =				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_							:       str	      =				outputs.logits

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

                      all_logits.append(lowerCAmelCase_      )

                      A_							:       Union[str, Any]	      =				torch.distributions.Bernoulli(logits=lowerCAmelCase_      )
                      A_							:       Optional[Any]	      =				dist_per_token.probs * attention_mask_example.type(torch.floataa      ).to(
                          dist_per_token.probs.device      )

                      A_							:       str	      =				collections.defaultdict(lowerCAmelCase_      )
                      for i, p in enumerate(probabilities.squeeze().tolist()      ):
                             A_							:       Union[str, Any]	      =				token_type_ids_example[:, 0].tolist()[i]
                             A_							:       str	      =				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_							:       Any	      =				{key: np.array(coords_to_probs[key]      ).mean() > 0.5 for key in coords_to_probs}

               A_							:       Optional[Any]	      =				torch.cat(tuple(lowerCAmelCase_      ) ,       0      )

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

               A_							:       str	      =				inputs['''input_ids''']
               A_							:       str	      =				inputs['''attention_mask''']
               A_							:       str	      =				inputs['''token_type_ids'''].numpy()
               A_							:       str	      =				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_							:       List[str]	      =				token_type_ids_example[:, 3]  # shape (seq_len,)
                             A_							:       List[Any]	      =				np.zeros_like(lowerCAmelCase_ ,       dtype=np.intaa      )  # shape (seq_len,)

                             A_							:       Optional[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_							:       List[str]	      =				token_type_ids_example[:, 1].tolist()[i] - 1
                                    A_							:       str	      =				token_type_ids_example[:, 2].tolist()[i] - 1

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

                             A_							:       Optional[Any]	      =				model_labels

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

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

                      all_logits.append(lowerCAmelCase_      )

                      A_							:       Dict	      =				tfp.distributions.Bernoulli(logits=lowerCAmelCase_      )
                      A_							:       str	      =				dist_per_token.probs_parameter() * tf.cast(lowerCAmelCase_ ,       tf.floataa      )

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

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

               A_							:       Dict	      =				tf.concat(tuple(lowerCAmelCase_      ) ,       0      )

               return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(lowerCAmelCase_      ) ,       0      ))
 def __call__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				self._args_parser(*lowerCAmelCase_ ,       **lowerCAmelCase_      )

        A_							:       List[Any]	      =				super().__call__(lowerCAmelCase_ ,       **lowerCAmelCase_      )
        if len(lowerCAmelCase_      ) == 1:
               return results[0]
        return results
 def  UpperCAmelCase   (    self ,       __magic_name__=None ,       __magic_name__=None ,       __magic_name__=None ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       List[str]	      =				{}
        if padding is not None:
               A_							:       Tuple	      =				padding
        if truncation is not None:
               A_							:       Any	      =				truncation

        A_							:       Tuple	      =				{}
        if sequential is not None:
               A_							:       Dict	      =				sequential
        return preprocess_params, forward_params, {}
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=True ,       __magic_name__=None      ):

        """simple docstring"""

        if truncation is None:
               if self.type == "tapas":
                      A_							:       str	      =				'''drop_rows_to_fit'''
               else:
                      A_							:       List[Any]	      =				'''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_							:       List[Any]	      =				self.tokenizer(lowerCAmelCase_ ,       lowerCAmelCase_ ,       return_tensors=self.framework ,       truncation=lowerCAmelCase_ ,       padding=lowerCAmelCase_      )
        A_							:       Any	      =				table
        return inputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=False      ):

        """simple docstring"""

        A_							:       int	      =				model_inputs.pop('''table'''      )

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

        """simple docstring"""

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

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

                      answers.append(lowerCAmelCase_      )
               if len(lowerCAmelCase_      ) == 0:
                      raise PipelineException('''Empty answer'''      )
        else:
               A_							:       Union[str, Any]	      =				[{'''answer''': answer} for answer in self.tokenizer.batch_decode(lowerCAmelCase_ ,       skip_special_tokens=lowerCAmelCase_      )]

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




'''simple docstring'''





import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["projector.weight"]
      snake_case_    							=				downstream_dict["projector.bias"]
      snake_case_    							=				downstream_dict["model.post_net.linear.weight"]
      snake_case_    							=				downstream_dict["model.post_net.linear.bias"]
      return model
def 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["connector.weight"]
      snake_case_    							=				downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel      ):
            snake_case_    							=				downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            snake_case_    							=				downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      snake_case_    							=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      snake_case_    							=				downstream_dict["objective.W"]
      return model
@torch.no_grad()
def 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''      )

      if hf_config.use_weighted_layer_sum:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


if __name__ == "__main__":
     A							     = argparse.ArgumentParser()
     parser.add_argument(
         "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
     )
     parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
     parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
     A							     = parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

