code: 
  
  
  
  """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 							_UpperCAmelCase							(				a__   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __call__(      self ,			A=None ,			A=None ,			**A	)							->      Optional[int]:
  									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(__A ,			__A	) and table.get('''query'''	) is not None and table.get('''table'''	) is not None:
  																	_UpperCAmelCase							:						List[str]       							=					[table]
  													elif isinstance(__A ,			__A	) and len(__A	) > 0:
  																	if not all(isinstance(__A ,			__A	) for d in table	):
  																					raise ValueError(
  																					    f'Keyword argument `table` should be a list of dict, but is {(type(__A	) for d in table)}'	)
  
  																	if table[0].get('''query'''	) is not None and table[0].get('''table'''	) is not None:
  																					_UpperCAmelCase							:						Dict       							=					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(__A ,			__A	) or isinstance(__A ,			types.GeneratorType	):
  																	return table
  													else:
  																	raise ValueError(
  																	    '''Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '''
  																	    f'is {type(__A	)})'	)
  									else:
  													_UpperCAmelCase							:						Tuple       							=					[{"table": table, "query": query}]
  
  									for tqa_pipeline_input in tqa_pipeline_inputs:
  													if not isinstance(tqa_pipeline_input['''table'''] ,			pd.DataFrame	):
  																	if tqa_pipeline_input["table"] is None:
  																					raise ValueError('''Table cannot be None.'''	)
  
  																	_UpperCAmelCase							:						List[str]       							=					pd.DataFrame(tqa_pipeline_input['''table''']	)
  
  									return tqa_pipeline_inputs
  
  
  
  @add_end_docstrings(a__   )
  class 							_UpperCAmelCase							(				a__   ):
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					a__             ='''table,query'''
  
  
  
  
  
  					def __init__(      self ,			A=TableQuestionAnsweringArgumentHandler() ,			*A ,			**A	)							->      Any:
  									super().__init__(*__A ,			**__A	)
  									_UpperCAmelCase							:						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()	)	)
  
  									_UpperCAmelCase							:						int       							=					bool(getattr(self.model.config ,			'''aggregation_labels''' ,			__A	)	) and bool(
  									    getattr(self.model.config ,			'''num_aggregation_labels''' ,			__A	)	)
  									_UpperCAmelCase							:						Tuple       							=					"tapas" if hasattr(self.model.config ,			'''aggregation_labels'''	) else None
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			**A	)							->      List[Any]:
  									return self.model(**__A	)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			**A	)							->      Dict:
  
  
  
  
  
  
  
  									if self.framework == "pt":
  													_UpperCAmelCase							:						Union[str, Any]       							=					[]
  													_UpperCAmelCase							:						List[str]       							=					[]
  													_UpperCAmelCase							:						Optional[int]       							=					None
  													_UpperCAmelCase							:						Tuple       							=					inputs["input_ids"].shape[0]
  
  													_UpperCAmelCase							:						Any       							=					inputs["input_ids"].to(self.device	)
  													_UpperCAmelCase							:						Union[str, Any]       							=					inputs["attention_mask"].to(self.device	)
  													_UpperCAmelCase							:						Any       							=					inputs["token_type_ids"].to(self.device	)
  													_UpperCAmelCase							:						Dict       							=					None
  
  													for index in range(__A	):
  																	# 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							:						List[str]       							=					token_type_ids_example[:, 3]  # shape (seq_len,)
  																					_UpperCAmelCase							:						List[str]       							=					np.zeros_like(prev_labels_example.cpu().numpy()	)  # shape (seq_len,)
  
  																					_UpperCAmelCase							:						Tuple       							=					token_type_ids[index]  # shape (seq_len, 7)
  																					for i in range(model_labels.shape[0]	):
  																									_UpperCAmelCase							:						List[Any]       							=					token_type_ids_example[:, 0].tolist()[i]
  																									_UpperCAmelCase							:						Tuple       							=					token_type_ids_example[:, 1].tolist()[i] - 1
  																									_UpperCAmelCase							:						List[str]       							=					token_type_ids_example[:, 2].tolist()[i] - 1
  
  																									if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																													_UpperCAmelCase							:						List[Any]       							=					int(prev_answers[(col_id, row_id)]	)
  
  																					_UpperCAmelCase							:						List[Any]       							=					torch.from_numpy(__A	).type(torch.long	).to(self.device	)
  
  																	_UpperCAmelCase							:						Dict       							=					input_ids[index]
  																	_UpperCAmelCase							:						Union[str, Any]       							=					attention_mask[index]  # shape (seq_len,)
  																	_UpperCAmelCase							:						Any       							=					token_type_ids[index]  # shape (seq_len, 7)
  																	_UpperCAmelCase							:						Any       							=					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							:						Optional[int]       							=					outputs.logits
  
  																	if self.aggregate:
  																					all_aggregations.append(outputs.logits_aggregation	)
  
  																	all_logits.append(__A	)
  
  																	_UpperCAmelCase							:						Dict       							=					torch.distributions.Bernoulli(logits=__A	)
  																	_UpperCAmelCase							:						Optional[int]       							=					dist_per_token.probs * attention_mask_example.type(torch.floataa	).to(
  																	    dist_per_token.probs.device	)
  
  																	_UpperCAmelCase							:						Dict       							=					collections.defaultdict(__A	)
  																	for i, p in enumerate(probabilities.squeeze().tolist()	):
  																					_UpperCAmelCase							:						int       							=					token_type_ids_example[:, 0].tolist()[i]
  																					_UpperCAmelCase							:						List[str]       							=					token_type_ids_example[:, 1].tolist()[i] - 1
  																					_UpperCAmelCase							:						Optional[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(__A	)
  
  																	_UpperCAmelCase							:						str       							=					{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}
  
  													_UpperCAmelCase							:						List[str]       							=					torch.cat(tuple(__A	) ,			0	)
  
  													return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(__A	) ,			0	))
  									else:
  													_UpperCAmelCase							:						List[Any]       							=					[]
  													_UpperCAmelCase							:						str       							=					[]
  													_UpperCAmelCase							:						Optional[Any]       							=					None
  													_UpperCAmelCase							:						Optional[Any]       							=					inputs["input_ids"].shape[0]
  
  													_UpperCAmelCase							:						List[Any]       							=					inputs["input_ids"]
  													_UpperCAmelCase							:						Dict       							=					inputs["attention_mask"]
  													_UpperCAmelCase							:						Union[str, Any]       							=					inputs["token_type_ids"].numpy()
  													_UpperCAmelCase							:						Optional[int]       							=					None
  
  													for index in range(__A	):
  																	# 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							:						Optional[Any]       							=					token_type_ids_example[:, 3]  # shape (seq_len,)
  																					_UpperCAmelCase							:						List[Any]       							=					np.zeros_like(__A ,			dtype=np.intaa	)  # shape (seq_len,)
  
  																					_UpperCAmelCase							:						Optional[int]       							=					token_type_ids[index]  # shape (seq_len, 7)
  																					for i in range(model_labels.shape[0]	):
  																									_UpperCAmelCase							:						Any       							=					token_type_ids_example[:, 0].tolist()[i]
  																									_UpperCAmelCase							:						Any       							=					token_type_ids_example[:, 1].tolist()[i] - 1
  																									_UpperCAmelCase							:						Union[str, Any]       							=					token_type_ids_example[:, 2].tolist()[i] - 1
  
  																									if row_id >= 0 and col_id >= 0 and segment_id == 1:
  																													_UpperCAmelCase							:						Dict       							=					int(prev_answers[(col_id, row_id)]	)
  
  																					_UpperCAmelCase							:						Optional[Any]       							=					model_labels
  
  																	_UpperCAmelCase							:						Union[str, Any]       							=					input_ids[index]
  																	_UpperCAmelCase							:						Any       							=					attention_mask[index]  # shape (seq_len,)
  																	_UpperCAmelCase							:						List[str]       							=					token_type_ids[index]  # shape (seq_len, 7)
  																	_UpperCAmelCase							:						Optional[Any]       							=					self.model(
  																	    input_ids=np.expand_dims(__A ,			axis=0	) ,			attention_mask=np.expand_dims(__A ,			axis=0	) ,			token_type_ids=np.expand_dims(__A ,			axis=0	) ,			)
  																	_UpperCAmelCase							:						List[Any]       							=					outputs.logits
  
  																	if self.aggregate:
  																					all_aggregations.append(outputs.logits_aggregation	)
  
  																	all_logits.append(__A	)
  
  																	_UpperCAmelCase							:						Union[str, Any]       							=					tfp.distributions.Bernoulli(logits=__A	)
  																	_UpperCAmelCase							:						Optional[int]       							=					dist_per_token.probs_parameter() * tf.cast(__A ,			tf.floataa	)
  
  																	_UpperCAmelCase							:						str       							=					collections.defaultdict(__A	)
  																	_UpperCAmelCase							:						int       							=					token_type_ids_example
  																	for i, p in enumerate(tf.squeeze(__A	).numpy().tolist()	):
  																					_UpperCAmelCase							:						Union[str, Any]       							=					token_type_ids_example[:, 0].tolist()[i]
  																					_UpperCAmelCase							:						int       							=					token_type_ids_example[:, 1].tolist()[i] - 1
  																					_UpperCAmelCase							:						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(__A	)
  
  																	_UpperCAmelCase							:						Dict       							=					{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}
  
  													_UpperCAmelCase							:						List[Any]       							=					tf.concat(tuple(__A	) ,			0	)
  
  													return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(__A	) ,			0	))
  
  
  
  
  
  					def __call__(      self ,			*A ,			**A	)							->      Any:
  									_UpperCAmelCase							:						Dict       							=					self._args_parser(*__A ,			**__A	)
  
  									_UpperCAmelCase							:						Optional[Any]       							=					super().__call__(__A ,			**__A	)
  									if len(__A	) == 1:
  													return results[0]
  									return results
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A=None ,			A=None ,			A=None ,			**A	)							->      Optional[Any]:
  									_UpperCAmelCase							:						Any       							=					{}
  									if padding is not None:
  													_UpperCAmelCase							:						int       							=					padding
  									if truncation is not None:
  													_UpperCAmelCase							:						List[str]       							=					truncation
  
  									_UpperCAmelCase							:						Optional[Any]       							=					{}
  									if sequential is not None:
  													_UpperCAmelCase							:						Any       							=					sequential
  									return preprocess_params, forward_params, {}
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A ,			A=None ,			A=True ,			A=None	)							->      Union[str, Any]:
  
  
  
  
  
  
  
  									if truncation is None:
  													if self.type == "tapas":
  																	_UpperCAmelCase							:						int       							=					"drop_rows_to_fit"
  													else:
  																	_UpperCAmelCase							:						str       							=					"do_not_truncate"
  
  									_UpperCAmelCase							:						str       							=					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							:						Any       							=					self.tokenizer(__A ,			__A ,			return_tensors=self.framework ,			truncation=__A ,			padding=__A	)
  									_UpperCAmelCase							:						Tuple       							=					table
  									return inputs
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A ,			A=False	)							->      int:
  									_UpperCAmelCase							:						List[str]       							=					model_inputs.pop('''table'''	)
  
  									if self.type == "tapas":
  													if sequential:
  																	_UpperCAmelCase							:						List[Any]       							=					self.sequential_inference(**__A	)
  													else:
  																	_UpperCAmelCase							:						Optional[Any]       							=					self.batch_inference(**__A	)
  									else:
  													_UpperCAmelCase							:						Tuple       							=					self.model.generate(**__A	)
  									_UpperCAmelCase							:						Any       							=					{"model_inputs": model_inputs, "table": table, "outputs": outputs}
  									return model_outputs
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self ,			A	)							->      List[Any]:
  									_UpperCAmelCase							:						Any       							=					model_outputs["model_inputs"]
  									_UpperCAmelCase							:						int       							=					model_outputs["table"]
  									_UpperCAmelCase							:						Any       							=					model_outputs["outputs"]
  									if self.type == "tapas":
  													if self.aggregate:
  																	_UpperCAmelCase							:						Any       							=					outputs[:2]
  																	_UpperCAmelCase							:						str       							=					self.tokenizer.convert_logits_to_predictions(__A ,			__A ,			__A	)
  																	_UpperCAmelCase							:						Tuple       							=					predictions
  																	_UpperCAmelCase							:						List[Any]       							=					{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(__A	)}
  
  																	_UpperCAmelCase							:						Dict       							=					self.model.config.no_aggregation_label_index
  																	_UpperCAmelCase							:						List[str]       							=					{
  																	    i: aggregators[i] + " > " for i, pred in enumerate(__A	) if pred != no_agg_label_index
  																	}
  													else:
  																	_UpperCAmelCase							:						Dict       							=					outputs[0]
  																	_UpperCAmelCase							:						Optional[Any]       							=					self.tokenizer.convert_logits_to_predictions(__A ,			__A	)
  																	_UpperCAmelCase							:						Union[str, Any]       							=					predictions[0]
  																	_UpperCAmelCase							:						int       							=					{}
  																	_UpperCAmelCase							:						Tuple       							=					{}
  													_UpperCAmelCase							:						Optional[int]       							=					[]
  													for index, coordinates in enumerate(__A	):
  																	_UpperCAmelCase							:						Tuple       							=					[table.iat[coordinate] for coordinate in coordinates]
  																	_UpperCAmelCase							:						Union[str, Any]       							=					aggregators.get(__A ,			''''''	)
  																	_UpperCAmelCase							:						int       							=					aggregators_prefix.get(__A ,			''''''	)
  																	_UpperCAmelCase							:						Tuple       							=					{
  																	    "answer": aggregator_prefix + ", ".join(__A	),
  																	    "coordinates": coordinates,
  																	    "cells": [table.iat[coordinate] for coordinate in coordinates],
  																	}
  																	if aggregator:
  																					_UpperCAmelCase							:						Tuple       							=					aggregator
  
  																	answers.append(__A	)
  													if len(__A	) == 0:
  																	raise PipelineException('''Empty answer'''	)
  									else:
  													_UpperCAmelCase							:						str       							=					[{"answer": answer} for answer in self.tokenizer.batch_decode(__A ,			skip_special_tokens=__A	)]
  
  									return answers if len(__A	) > 1 else answers[0]
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  _lowerCAmelCase					:int  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Dict  =      {
      'post_extract_proj': 'feature_projection',
      'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
      'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
      'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
      'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
      'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
      'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
      'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
      'output.dense': 'encoder.encoder.layer.*.output.dense',
      'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
      'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
      'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
      'encoder.upsample.0': 'encoder.upsample.projection',
      'encoder.layer_norm': 'encoder.layer_norm',
      'w2v_model.layer_norm': 'layer_norm',
      'w2v_encoder.proj': 'lm_head',
      'mask_emb': 'masked_spec_embed',
  }
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
      for attribute in key.split('''.'''							):
          _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)
  
      if weight_type is not None:
          _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).shape
      else:
          _UpperCAmelCase							:						Union[str, Any]       							=					hf_pointer.shape
  
      assert hf_shape == value.shape, (
          F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
          F' {value.shape} for {full_name}'
      )
  
      if weight_type == "weight":
          _UpperCAmelCase							:						Dict       							=					value
      elif weight_type == "weight_g":
          _UpperCAmelCase							:						Optional[int]       							=					value
      elif weight_type == "weight_v":
          _UpperCAmelCase							:						List[Any]       							=					value
      elif weight_type == "bias":
          _UpperCAmelCase							:						Union[str, Any]       							=					value
      else:
          _UpperCAmelCase							:						Optional[int]       							=					value
  
      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						Dict       							=					[]
      _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()
  
      _UpperCAmelCase							:						Any       							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
      for name, value in fairseq_dict.items():
          _UpperCAmelCase							:						str       							=					False
          if "conv_layers" in name:
              load_conv_layer(
                  UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , hf_model.config.feat_extract_norm == '''group'''  , )
              _UpperCAmelCase							:						Any       							=					True
          else:
              for key, mapped_key in MAPPING.items():
                  _UpperCAmelCase							:						str       							=					'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
  
                  if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
                      _UpperCAmelCase							:						Dict       							=					True
                      if "*" in mapped_key:
                          _UpperCAmelCase							:						List[str]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                          if not layer_index.isnumeric():
                              continue
                          _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                      if "weight_g" in name:
                          _UpperCAmelCase							:						int       							=					'''weight_g'''
                      elif "weight_v" in name:
                          _UpperCAmelCase							:						Optional[int]       							=					'''weight_v'''
                      elif "weight" in name:
                          _UpperCAmelCase							:						int       							=					'''weight'''
                      elif "bias" in name:
                          _UpperCAmelCase							:						int       							=					'''bias'''
                      else:
                          _UpperCAmelCase							:						Dict       							=					None
                      set_recursively(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
                  continue
          if not is_used:
              unused_weights.append(UpperCamelCase__							)
  
      logger.warning(F'Unused weights: {unused_weights}'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
      _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
      _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
      _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
      _UpperCAmelCase							:						int       							=					int(items[1]							)
  
      if type_id == 0:
          if "bias" in name:
              assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                  F'{full_name} has size {value.shape}, but'
                  F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
              )
              _UpperCAmelCase							:						Tuple       							=					value
              logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
          elif "weight" in name:
              assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                  F'{full_name} has size {value.shape}, but'
                  F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
              )
              _UpperCAmelCase							:						Union[str, Any]       							=					value
              logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
      elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
              assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                  F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                  " found."
              )
              _UpperCAmelCase							:						Union[str, Any]       							=					value
              logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
          elif "weight" in name:
              assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                  F'{full_name} has size {value.shape}, but'
                  F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
              )
              _UpperCAmelCase							:						int       							=					value
              logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
      else:
          unused_weights.append(UpperCamelCase__							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Any							):
      _UpperCAmelCase							:						Any       							=					SEWDConfig()
      if is_finetuned:
          _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					model.cfg
  
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
      _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
      _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
      _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					'''gelu'''
      _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      _UpperCAmelCase							:						Tuple       							=					0.0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
      _UpperCAmelCase							:						Optional[int]       							=					0.02
      _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
      _UpperCAmelCase							:						Dict       							=					1E-5
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
      _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
      _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
      _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
      _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
      _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
      _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
      _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
      _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
      _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
      _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
      _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd
  
      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
          _UpperCAmelCase							:						Any       							=					model.cfg
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.final_dropout
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.layerdrop
      _UpperCAmelCase							:						str       							=					fs_config.activation_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.attention_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.dropout_input
      _UpperCAmelCase							:						str       							=					fs_config.dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_channel_length
      _UpperCAmelCase							:						Tuple       							=					fs_config.mask_channel_prob
      _UpperCAmelCase							:						List[str]       							=					fs_config.mask_length
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.mask_prob
  
      _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
      _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''
  
      return config
  
  
  
  
  
  @torch.no_grad()
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):
  
      if is_finetuned:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						int       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
      else:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)
  
      if config_path is not None:
          _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					model[0].eval()
  
      _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
      _UpperCAmelCase							:						Optional[int]       							=					WavaVecaFeatureExtractor(
          feature_size=1  , sampling_rate=1_6000  , padding_value=0  , do_normalize=UpperCamelCase__  , return_attention_mask=UpperCamelCase__  , )
  
      if is_finetuned:
          if dict_path:
              _UpperCAmelCase							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)
  
              # important change bos & pad token id since CTC symbol is <pad> and
              # not <s> as in fairseq
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
              _UpperCAmelCase							:						int       							=					target_dict.pad_index
              _UpperCAmelCase							:						str       							=					target_dict.bos_index
              _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
              _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
              _UpperCAmelCase							:						Optional[Any]       							=					os.path.join(UpperCamelCase__  , '''vocab.json'''							)
              if not os.path.isdir(UpperCamelCase__							):
                  logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__							)							)
                  return
              os.makedirs(UpperCamelCase__  , exist_ok=UpperCamelCase__							)
              with open(UpperCamelCase__  , '''w'''  , encoding='''utf-8'''							) as vocab_handle:
                  json.dump(target_dict.indices  , UpperCamelCase__							)
              _UpperCAmelCase							:						Any       							=					WavaVecaCTCTokenizer(
                  UpperCamelCase__  , unk_token=target_dict.unk_word  , pad_token=target_dict.pad_word  , bos_token=target_dict.bos_word  , eos_token=target_dict.eos_word  , word_delimiter_token='''|'''  , do_lower_case=UpperCamelCase__  , )
              _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
              processor.save_pretrained(UpperCamelCase__							)
  
          _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
          feature_extractor.save_pretrained(UpperCamelCase__							)
  
      recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      hf_model.save_pretrained(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Union[str, Any]  =      argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    _lowerCAmelCase					:int  =      parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )
  
  
  