

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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase__		      =       {
    "post_extract_proj": "feature_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.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__							(		SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							Tuple					)	->					Optional[int]:




				'''simple docstring'''





				for attribute in key.split("."					):
								A__						=	getattr(__lowerCAmelCase		,				__lowerCAmelCase					)

				if weight_type is not None:
								A__						=	getattr(__lowerCAmelCase		,				__lowerCAmelCase					).shape
				else:
								A__						=	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":
								A__						=	value
				elif weight_type == "weight_g":
								A__						=	value
				elif weight_type == "weight_v":
								A__						=	value
				elif weight_type == "bias":
								A__						=	value
				else:
								A__						=	value

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[Any]					)	->					List[str]:




				'''simple docstring'''





				A__						=	[]
				A__						=	fairseq_model.state_dict()

				A__						=	hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								A__						=	False
								if "conv_layers" in name:
												load_conv_layer(
												    __lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase		,				hf_model.config.feat_extract_norm == "group"		,				)
												A__						=	True
								else:
												for key, mapped_key in MAPPING.items():
																A__						=	"sew." + 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]:
																				A__						=	True
																				if "*" in mapped_key:
																								A__						=	name.split(__lowerCAmelCase					)[0].split("."					)[-2]
																								A__						=	mapped_key.replace("*"		,				__lowerCAmelCase					)
																				if "weight_g" in name:
																								A__						=	"weight_g"
																				elif "weight_v" in name:
																								A__						=	"weight_v"
																				elif "weight" in name:
																								A__						=	"weight"
																				elif "bias" in name:
																								A__						=	"bias"
																				else:
																								A__						=	None
																				set_recursively(__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase					)
																continue
								if not is_used:
												unused_weights.append(__lowerCAmelCase					)

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



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Optional[Any]		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							Tuple		,				SCREAMING_SNAKE_CASE_:							List[Any]					)	->					Optional[Any]:




				'''simple docstring'''





				A__						=	full_name.split("conv_layers."					)[-1]
				A__						=	name.split("."					)
				A__						=	int(items[0]					)
				A__						=	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.'
												)
												A__						=	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.'
												)
												A__						=	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."
												)
												A__						=	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.'
												)
												A__						=	value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
				else:
								unused_weights.append(__lowerCAmelCase					)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[int]		,				SCREAMING_SNAKE_CASE_:							List[str]					)	->					List[Any]:




				'''simple docstring'''





				A__						=	SEWConfig()
				if is_finetuned:
								A__						=	model.wav_encoder.wav_model.cfg
				else:
								A__						=	model.cfg

				A__						=	fs_config.conv_bias
				A__						=	eval(fs_config.conv_feature_layers					)
				A__						=	[x[0] for x in conv_layers]
				A__						=	[x[1] for x in conv_layers]
				A__						=	[x[2] for x in conv_layers]
				A__						=	"gelu"
				A__						=	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
				A__						=	0.0
				A__						=	fs_config.activation_fn.name
				A__						=	fs_config.encoder_embed_dim
				A__						=	0.02
				A__						=	fs_config.encoder_ffn_embed_dim
				A__						=	1e-5
				A__						=	fs_config.encoder_layerdrop
				A__						=	fs_config.encoder_attention_heads
				A__						=	fs_config.conv_pos_groups
				A__						=	fs_config.conv_pos
				A__						=	len(__lowerCAmelCase					)
				A__						=	fs_config.encoder_layers
				A__						=	fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								A__						=	model.cfg
								A__						=	fs_config.final_dropout
								A__						=	fs_config.layerdrop
				A__						=	fs_config.activation_dropout
				A__						=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				A__						=	fs_config.attention_dropout
				A__						=	fs_config.dropout_input
				A__						=	fs_config.dropout
				A__						=	fs_config.mask_channel_length
				A__						=	fs_config.mask_channel_prob
				A__						=	fs_config.mask_length
				A__						=	fs_config.mask_prob

				A__						=	"Wav2Vec2FeatureExtractor"
				A__						=	"Wav2Vec2CTCTokenizer"

				return config



@torch.no_grad()
def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Union[str, Any]		,				SCREAMING_SNAKE_CASE_:							str		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,				SCREAMING_SNAKE_CASE_:							Union[str, Any]=None		,				SCREAMING_SNAKE_CASE_:							Tuple=True					)	->					int:




				'''simple docstring'''





				if is_finetuned:
								A__			, A__			, A__						=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]		,				arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
				else:
								A__			, A__			, A__						=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

				if config_path is not None:
								A__						=	SEWConfig.from_pretrained(__lowerCAmelCase					)
				else:
								A__						=	convert_config(model[0]		,				__lowerCAmelCase					)
				A__						=	model[0].eval()

				A__						=	True if config.feat_extract_norm == "layer" else False
				A__						=	WavaVecaFeatureExtractor(
				    feature_size=1		,				sampling_rate=1_6_0_0_0		,				padding_value=0		,				do_normalize=__lowerCAmelCase		,				return_attention_mask=__lowerCAmelCase		,				)

				if is_finetuned:
								if dict_path:
												A__						=	Dictionary.load(__lowerCAmelCase					)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												A__						=	target_dict.pad_index
												A__						=	target_dict.bos_index
												A__						=	target_dict.pad_index
												A__						=	target_dict.bos_index
												A__						=	target_dict.eos_index
												A__						=	len(target_dict.symbols					)
												A__						=	os.path.join(__lowerCAmelCase		,				"vocab.json"					)
												if not os.path.isdir(__lowerCAmelCase					):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__lowerCAmelCase					)					)
																return
												os.makedirs(__lowerCAmelCase		,				exist_ok=__lowerCAmelCase					)
												with open(__lowerCAmelCase		,				"w"		,				encoding="utf-8"					) as vocab_handle:
																json.dump(target_dict.indices		,				__lowerCAmelCase					)
												A__						=	WavaVecaCTCTokenizer(
												    __lowerCAmelCase		,				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=__lowerCAmelCase		,				)
												A__						=	WavaVecaProcessor(feature_extractor=__lowerCAmelCase		,				tokenizer=__lowerCAmelCase					)
												processor.save_pretrained(__lowerCAmelCase					)

								A__						=	SEWForCTC(__lowerCAmelCase					)
				else:
								A__						=	SEWModel(__lowerCAmelCase					)
								feature_extractor.save_pretrained(__lowerCAmelCase					)

				recursively_load_weights(__lowerCAmelCase		,				__lowerCAmelCase		,				__lowerCAmelCase					)

				hf_model.save_pretrained(__lowerCAmelCase					)


if __name__ == "__main__":
	lowerCAmelCase__		      =       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__		      =       parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)



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


SCREAMING_SNAKE_CASE__   :			Optional[int]												=    logging.getLogger(__name__)

@dataclass
class    lowerCAmelCase__     :
		a__       :						str
		a__       :						List[str]
		a__       :						Optional[List[str]]





@dataclass
class    lowerCAmelCase__     :
		a__       :						List[int]
		a__       :						List[int]
		a__       :						Optional[List[int]]        =		None
		a__       :						Optional[List[int]]        =		None





class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Optional[Any]        =		"""train"""
		a__       :						Optional[int]        =		"""dev"""
		a__       :						Dict        =		"""test"""





class    lowerCAmelCase__     :






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[Split, str]  )     ->							List[InputExample]:
						raise NotImplementedError






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						raise NotImplementedError







		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							List[InputExample]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							List[str]="[CLS]"							,			SCREAMING_SNAKE_CASE__		:							Tuple=1							,			SCREAMING_SNAKE_CASE__		:							str="[SEP]"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Tuple=0							,			SCREAMING_SNAKE_CASE__		:							int=0							,			SCREAMING_SNAKE_CASE__		:							str=-1_00							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=0							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			)     ->							List[InputFeatures]:
						__lowerCamelCase    					=	{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__  )}

						__lowerCamelCase    					=	[]
						for ex_index, example in enumerate(SCREAMING_SNAKE_CASE__  ):
										if ex_index % 1_00_00 == 0:
														logger.info('''Writing example %d of %d'''							,			SCREAMING_SNAKE_CASE__							,			len(SCREAMING_SNAKE_CASE__  )  )

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

														# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
														if len(SCREAMING_SNAKE_CASE__  ) > 0:
																		tokens.extend(SCREAMING_SNAKE_CASE__  )
																		# 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(SCREAMING_SNAKE_CASE__  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
										__lowerCamelCase    					=	tokenizer.num_special_tokens_to_add()
										if len(SCREAMING_SNAKE_CASE__  ) > max_seq_length - special_tokens_count:
														__lowerCamelCase    					=	tokens[: (max_seq_length - special_tokens_count)]
														__lowerCamelCase    					=	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]
										__lowerCamelCase    					=	[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE__  )

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

										__lowerCamelCase    					=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )

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

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

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

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

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


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

					class    lowerCAmelCase__     (	__lowercase			):
							a__       :						List[InputFeatures]
							a__       :						int        =		nn.CrossEntropyLoss().ignore_index






							def __init__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							Union[str, Any]:
											# Load data features from cache or dataset file
											__lowerCamelCase    					=	os.path.join(
											    SCREAMING_SNAKE_CASE__							,			'''cached_{}_{}_{}'''.format(mode.value							,			tokenizer.__class__.__name__							,			str(SCREAMING_SNAKE_CASE__  )  )							,			)

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											__lowerCamelCase    					=	cached_features_file + '''.lock'''
											with FileLock(SCREAMING_SNAKE_CASE__  ):
															if os.path.exists(SCREAMING_SNAKE_CASE__  ) and not overwrite_cache:
																			logger.info(f'''Loading features from cached file {cached_features_file}'''  )
																			__lowerCamelCase    					=	torch.load(SCREAMING_SNAKE_CASE__  )
															else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''  )
																			__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
																			# TODO clean up all this to leverage built-in features of tokenizers
																			__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
																			    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			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							,			SCREAMING_SNAKE_CASE__  )






							def __len__(		self		:							Dict  )     ->							str:
											return len(self.features  )







							def __getitem__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							InputFeatures:
											return self.features[i]


if is_tf_available():
					import tensorflow as tf

					class    lowerCAmelCase__     :
							a__       :						List[InputFeatures]
							a__       :						int        =		-100






							def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							List[Any]:
											__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
											    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			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:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)							,			(
															        {'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )},
															        tf.TensorShape([None]  ),
															    )							,			)
											else:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''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  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
											__lowerCamelCase    					=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

											return self.dataset






							def __len__(		self		:							List[Any]  )     ->							Any:
											return len(self.features  )







							def __getitem__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							InputFeatures:
											return self.features[i]



