





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
snake_case								=							logging.get_logger(__name__)

snake_case								=							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
snake_case								=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any  )   ->    Tuple:
  _lowercase						    =			{}
  with open(snake_case__      ,				'r'  ) as file:
    for line_number, line in enumerate(snake_case__  ):
      _lowercase						    =			line.strip()
      if line:
        _lowercase						    =			line.split()
        _lowercase						    =			line_number
        _lowercase						    =			words[0]
        _lowercase						    =			value
  return result

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[Any]      ,				snake_case__	:Optional[int]  )   ->    List[str]:
  for attribute in key.split('.'  ):
    _lowercase						    =			getattr(snake_case__      ,				snake_case__  )

  _lowercase						    =			None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__  ):
      _lowercase						    =			PARAM_MAPPING[full_name.split('.'  )[-1]]
      _lowercase						    =			'param'

  if weight_type is not None and weight_type != "param":
    _lowercase						    =			getattr(snake_case__      ,				snake_case__  ).shape
  elif weight_type is not None and weight_type == "param":
    _lowercase						    =			hf_pointer
    for attribute in hf_param_name.split('.'  ):
      _lowercase						    =			getattr(snake_case__      ,				snake_case__  )
    _lowercase						    =			shape_pointer.shape

    # let's reduce dimension
    _lowercase						    =			value[0]
  else:
    _lowercase						    =			hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        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":
    _lowercase						    =			value
  elif weight_type == "weight_g":
    _lowercase						    =			value
  elif weight_type == "weight_v":
    _lowercase						    =			value
  elif weight_type == "bias":
    _lowercase						    =			value
  elif weight_type == "param":
    for attribute in hf_param_name.split('.'  ):
      _lowercase						    =			getattr(snake_case__      ,				snake_case__  )
    _lowercase						    =			value
  else:
    _lowercase						    =			value

  logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:str      ,				snake_case__	:Dict      ,				snake_case__	:List[str]      ,				snake_case__	:Dict  )   ->    List[Any]:
  _lowercase						    =			None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__  ):
      _lowercase						    =			PARAM_MAPPING[full_name.split('.'  )[-1]]
      _lowercase						    =			'param'

  if weight_type is not None and weight_type != "param":
    _lowercase						    =			'.'.join([key, weight_type]  )
  elif weight_type is not None and weight_type == "param":
    _lowercase						    =			'.'.join([key, hf_param_name]  )
  else:
    _lowercase						    =			key

  _lowercase						    =			value if 'lm_head' in full_key else value[0]


snake_case								=							{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Dict      ,				snake_case__	:List[str]      ,				snake_case__	:Optional[int]=None      ,				snake_case__	:Tuple=None  )   ->    Any:
  _lowercase						    =			False
  for key, mapped_key in MAPPING.items():
    _lowercase						    =			'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
      _lowercase						    =			True
      if "*" in mapped_key:
        _lowercase						    =			name.split(snake_case__  )[0].split('.'  )[-2]
        _lowercase						    =			mapped_key.replace('*'      ,				snake_case__  )
      if "weight_g" in name:
        _lowercase						    =			'weight_g'
      elif "weight_v" in name:
        _lowercase						    =			'weight_v'
      elif "bias" in name:
        _lowercase						    =			'bias'
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _lowercase						    =			'weight'
      else:
        _lowercase						    =			None
      if hf_dict is not None:
        rename_dict(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__  )
      else:
        set_recursively(snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__  )
      return is_used
  return is_used

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:List[Any]      ,				snake_case__	:int      ,				snake_case__	:Dict  )   ->    Tuple:
  _lowercase						    =			[]
  _lowercase						    =			fairseq_model.state_dict()

  _lowercase						    =			hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _lowercase						    =			False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__      ,				snake_case__      ,				snake_case__      ,				snake_case__      ,				hf_model.config.feat_extract_norm == 'group'      ,				)
      _lowercase						    =			True
    else:
      _lowercase						    =			load_wavaveca_layer(snake_case__      ,				snake_case__      ,				snake_case__  )
    if not is_used:
      unused_weights.append(snake_case__  )

  logger.warning(F"""Unused weights: {unused_weights}"""  )

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Tuple      ,				snake_case__	:List[str]      ,				snake_case__	:Optional[Any]      ,				snake_case__	:List[str]      ,				snake_case__	:List[Any]  )   ->    Union[str, Any]:
  _lowercase						    =			full_name.split('conv_layers.'  )[-1]
  _lowercase						    =			name.split('.'  )
  _lowercase						    =			int(items[0]  )
  _lowercase						    =			int(items[1]  )

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            F"""{full_name} has size {value.shape}, but"""
            F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
      _lowercase						    =			value
      logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            F"""{full_name} has size {value.shape}, but"""
            F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
      _lowercase						    =			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:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            F"""{full_name} has size {value.shape}, but"""
            F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""  )
      _lowercase						    =			value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            F"""{full_name} has size {value.shape}, but"""
            F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""  )
      _lowercase						    =			value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
  else:
    unused_weights.append(snake_case__  )

@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:int      ,				snake_case__	:Dict      ,				snake_case__	:Optional[Any]=None      ,				snake_case__	:int=None      ,				snake_case__	:Dict=True      ,				snake_case__	:str=False  )   ->    int:
  if config_path is not None:
    _lowercase						    =			WavaVecaConfig.from_pretrained(snake_case__  )
  else:
    _lowercase						    =			WavaVecaConfig()

  if is_seq_class:
    _lowercase						    =			read_txt_into_dict(snake_case__  )
    _lowercase						    =			idalabel
    _lowercase						    =			WavaVecaForSequenceClassification(snake_case__  )
    _lowercase						    =			WavaVecaFeatureExtractor(
        feature_size=1      ,				sampling_rate=1_6000      ,				padding_value=0      ,				do_normalize=snake_case__      ,				return_attention_mask=snake_case__      ,				)
    feature_extractor.save_pretrained(snake_case__  )

  elif is_finetuned:
    if dict_path:
      _lowercase						    =			Dictionary.load(snake_case__  )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowercase						    =			target_dict.pad_index
      _lowercase						    =			target_dict.bos_index
      _lowercase						    =			target_dict.eos_index
      _lowercase						    =			len(target_dict.symbols  )
      _lowercase						    =			os.path.join(snake_case__      ,				'vocab.json'  )
      if not os.path.isdir(snake_case__  ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case__  )  )
        return
      os.makedirs(snake_case__      ,				exist_ok=snake_case__  )
      _lowercase						    =			target_dict.indices

      # fairseq has the <pad> and <s> switched
      _lowercase						    =			0
      _lowercase						    =			1
      with open(snake_case__      ,				'w'      ,				encoding='utf-8'  ) as vocab_handle:
        json.dump(snake_case__      ,				snake_case__  )
      _lowercase						    =			WavaVecaCTCTokenizer(
          snake_case__      ,				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=snake_case__      ,				)
      _lowercase						    =			True if config.feat_extract_norm == 'layer' else False
      _lowercase						    =			WavaVecaFeatureExtractor(
          feature_size=1      ,				sampling_rate=1_6000      ,				padding_value=0      ,				do_normalize=snake_case__      ,				return_attention_mask=snake_case__      ,				)
      _lowercase						    =			WavaVecaProcessor(feature_extractor=snake_case__      ,				tokenizer=snake_case__  )
      processor.save_pretrained(snake_case__  )

    _lowercase						    =			WavaVecaForCTC(snake_case__  )
  else:
    _lowercase						    =			WavaVecaForPreTraining(snake_case__  )

  if is_finetuned or is_seq_class:
    _lowercase    ,		_lowercase    ,		_lowercase						    =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]      ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
  else:
    _lowercase						    =			argparse.Namespace(task='audio_pretraining'  )
    _lowercase						    =			fairseq.tasks.setup_task(snake_case__  )

    _lowercase    ,		_lowercase    ,		_lowercase						    =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      ,				task=snake_case__  )

  _lowercase						    =			model[0].eval()

  recursively_load_weights(snake_case__      ,				snake_case__      ,				not is_finetuned  )

  hf_wavavec.save_pretrained(snake_case__  )


if __name__ == "__main__":
      snake_case								=							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(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      parser.add_argument(
          """--is_seq_class""",
          action="""store_true""",
          help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
      )
      snake_case								=							parser.parse_args()

      snake_case								=							not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


UpperCAmelCase__							  =						logging.getLogger(__name__)






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    42






@dataclass
class 						lowercase_   :






      '''simple docstring'''




      __snake_case	    =    42
      __snake_case	    =    42
      __snake_case	    =    None
      __snake_case	    =    None






class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      __snake_case	    =    '''train'''
      __snake_case	    =    '''dev'''
      __snake_case	    =    '''test'''






class 						lowercase_   :






      '''simple docstring'''




      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		int					,   __UpperCAmelCase :		Union[Split, str]    )       ->List[InputExample]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		str    )       ->List[str]:




       """simple docstring"""


       raise NotImplementedError







      @staticmethod
      def  __lowerCAmelCase   ( __UpperCAmelCase :		List[InputExample]					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		str="[CLS]"					,   __UpperCAmelCase :		str=1					,   __UpperCAmelCase :		List[str]="[SEP]"					,   __UpperCAmelCase :		Union[str, Any]=False					,   __UpperCAmelCase :		Tuple=False					,   __UpperCAmelCase :		List[str]=0					,   __UpperCAmelCase :		int=0					,   __UpperCAmelCase :		Tuple=-100					,   __UpperCAmelCase :		Optional[int]=0					,   __UpperCAmelCase :		Any=True					,   )       ->List[InputFeatures]:




       """simple docstring"""


       a						    = {label: i for i, label in enumerate(__UpperCAmelCase    )}

       a						    = []
       for ex_index, example in enumerate(__UpperCAmelCase    ):
        if ex_index % 10_000 == 0:
         logger.info('''Writing example %d of %d'''					,   __UpperCAmelCase					,   len(__UpperCAmelCase    )    )

        a						    = []
        a						    = []
        for word, label in zip(example.words					,   example.labels    ):
         a						    = tokenizer.tokenize(__UpperCAmelCase    )

         # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
         if len(__UpperCAmelCase    ) > 0:
          tokens.extend(__UpperCAmelCase    )
          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__UpperCAmelCase    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
        a						    = tokenizer.num_special_tokens_to_add()
        if len(__UpperCAmelCase    ) > max_seq_length - special_tokens_count:
         a						    = tokens[: (max_seq_length - special_tokens_count)]
         a						    = label_ids[: (max_seq_length - special_tokens_count)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens += [sep_token]
        label_ids += [pad_token_label_id]
        if sep_token_extra:
         # roberta uses an extra separator b/w pairs of sentences
         tokens += [sep_token]
         label_ids += [pad_token_label_id]
        a						    = [sequence_a_segment_id] * len(__UpperCAmelCase    )

        if cls_token_at_end:
         tokens += [cls_token]
         label_ids += [pad_token_label_id]
         segment_ids += [cls_token_segment_id]
        else:
         a						    = [cls_token] + tokens
         a						    = [pad_token_label_id] + label_ids
         a						    = [cls_token_segment_id] + segment_ids

        a						    = tokenizer.convert_tokens_to_ids(__UpperCAmelCase    )

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        a						    = [1 if mask_padding_with_zero else 0] * len(__UpperCAmelCase    )

        # Zero-pad up to the sequence length.
        a						    = max_seq_length - len(__UpperCAmelCase    )
        if pad_on_left:
         a						    = ([pad_token] * padding_length) + input_ids
         a						    = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
         a						    = ([pad_token_segment_id] * padding_length) + segment_ids
         a						    = ([pad_token_label_id] * padding_length) + label_ids
        else:
         input_ids += [pad_token] * padding_length
         input_mask += [0 if mask_padding_with_zero else 1] * padding_length
         segment_ids += [pad_token_segment_id] * padding_length
         label_ids += [pad_token_label_id] * padding_length

        assert len(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length
        assert len(__UpperCAmelCase    ) == max_seq_length

        if ex_index < 5:
         logger.info('''*** Example ***'''    )
         logger.info('''guid: %s'''					,   example.guid    )
         logger.info('''tokens: %s'''					,   ''' '''.join([str(__UpperCAmelCase    ) for x in tokens]    )    )
         logger.info('''input_ids: %s'''					,   ''' '''.join([str(__UpperCAmelCase    ) for x in input_ids]    )    )
         logger.info('''input_mask: %s'''					,   ''' '''.join([str(__UpperCAmelCase    ) for x in input_mask]    )    )
         logger.info('''segment_ids: %s'''					,   ''' '''.join([str(__UpperCAmelCase    ) for x in segment_ids]    )    )
         logger.info('''label_ids: %s'''					,   ''' '''.join([str(__UpperCAmelCase    ) for x in label_ids]    )    )

        if "token_type_ids" not in tokenizer.model_input_names:
         a						    = None

        features.append(
            InputFeatures(
                input_ids=__UpperCAmelCase					,   attention_mask=__UpperCAmelCase					,   token_type_ids=__UpperCAmelCase					,   label_ids=__UpperCAmelCase    )    )
       return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset






     class 						lowercase_   (      lowercase  ):






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    nn.CrossEntropyLoss().ignore_index







           def __init__( self :		str					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		Any=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->List[str]:




            """simple docstring"""


            a						    = os.path.join(
                __UpperCAmelCase					,   '''cached_{}_{}_{}'''.format(mode.value					,   tokenizer.__class__.__name__					,   str(__UpperCAmelCase    )    )					,   )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            a						    = cached_features_file + '''.lock'''
            with FileLock(__UpperCAmelCase    ):
             if os.path.exists(__UpperCAmelCase    ) and not overwrite_cache:
              logger.info(F"""Loading features from cached file {cached_features_file}"""    )
              a						    = torch.load(__UpperCAmelCase    )
             else:
              logger.info(F"""Creating features from dataset file at {data_dir}"""    )
              a						    = token_classification_task.read_examples_from_file(__UpperCAmelCase					,   __UpperCAmelCase    )
              # TODO clean up all this to leverage built-in features of tokenizers
              a						    = token_classification_task.convert_examples_to_features(
                  __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   cls_token_at_end=bool(model_type in ['''xlnet''']    )					,   cls_token=tokenizer.cls_token					,   cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,   sep_token=tokenizer.sep_token					,   sep_token_extra=__UpperCAmelCase					,   pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,   pad_token=tokenizer.pad_token_id					,   pad_token_segment_id=tokenizer.pad_token_type_id					,   pad_token_label_id=self.pad_token_label_id					,   )
              logger.info(F"""Saving features into cached file {cached_features_file}"""    )
              torch.save(self.features					,   __UpperCAmelCase    )







           def __len__( self :		List[str]    )       ->Union[str, Any]:




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		List[Any]					,   __UpperCAmelCase :		Dict    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


if is_tf_available():
     import tensorflow as tf






     class 						lowercase_   :






           '''simple docstring'''




           __snake_case	    =    42
           __snake_case	    =    -1_00







           def __init__( self :		Any					,   __UpperCAmelCase :		TokenClassificationTask					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		PreTrainedTokenizer					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		Optional[int] = None					,   __UpperCAmelCase :		List[Any]=False					,   __UpperCAmelCase :		Split = Split.train					,   )       ->Optional[int]:




            """simple docstring"""


            a						    = token_classification_task.read_examples_from_file(__UpperCAmelCase					,   __UpperCAmelCase    )
            # TODO clean up all this to leverage built-in features of tokenizers
            a						    = token_classification_task.convert_examples_to_features(
                __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   __UpperCAmelCase					,   cls_token_at_end=bool(model_type in ['''xlnet''']    )					,   cls_token=tokenizer.cls_token					,   cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0					,   sep_token=tokenizer.sep_token					,   sep_token_extra=__UpperCAmelCase					,   pad_on_left=bool(tokenizer.padding_side == '''left'''    )					,   pad_token=tokenizer.pad_token_id					,   pad_token_segment_id=tokenizer.pad_token_type_id					,   pad_token_label_id=self.pad_token_label_id					,   )

            def gen():
             for ex in self.features:
              if ex.token_type_ids is None:
               yield (
                   {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                   ex.label_ids,
               )
              else:
               yield (
                   {
                       "input_ids": ex.input_ids,
                       "attention_mask": ex.attention_mask,
                       "token_type_ids": ex.token_type_ids,
                   },
                   ex.label_ids,
               )

            if "token_type_ids" not in tokenizer.model_input_names:
             a						    = tf.data.Dataset.from_generator(
                 __UpperCAmelCase					,   ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)					,   (
                     {'''input_ids''': tf.TensorShape([None]    ), '''attention_mask''': tf.TensorShape([None]    )},
                     tf.TensorShape([None]    ),
                 )					,   )
            else:
             a						    = tf.data.Dataset.from_generator(
                 __UpperCAmelCase					,   ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)					,   (
                     {
                         '''input_ids''': tf.TensorShape([None]    ),
                         '''attention_mask''': tf.TensorShape([None]    ),
                         '''token_type_ids''': tf.TensorShape([None]    ),
                     },
                     tf.TensorShape([None]    ),
                 )					,   )







           def  __lowerCAmelCase   ( self :		str    )       ->Optional[int]:




            """simple docstring"""


            a						    = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

            return self.dataset







           def __len__( self :		List[str]    )       ->Dict:




            """simple docstring"""


            return len(self.features    )







           def __getitem__( self :		Tuple					,   __UpperCAmelCase :		List[Any]    )       ->InputFeatures:




            """simple docstring"""


            return self.features[i]


