import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__lowercase										= logging.get_logger('''transformers.models.speecht5''')

__lowercase										= {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__lowercase										= {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__lowercase										= {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__lowercase										= {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__lowercase										= {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowercase										= {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowercase										= {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__lowercase										= {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__lowercase										= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowercase										= {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowercase										= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowercase										= []
__lowercase										= [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
__lowercase										= IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowercase										= IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowercase										= IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for attribute in key.split('''.'''     ):
        __UpperCamelCase					:List[Any]        =		getattr(lowerCAmelCase__  ,      lowerCAmelCase__     )

    if weight_type is not None:
        __UpperCamelCase					:int        =		getattr(lowerCAmelCase__  ,      lowerCAmelCase__     ).shape
    else:
        __UpperCamelCase					:int        =		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":
        __UpperCamelCase					:Dict        =		value
    elif weight_type == "weight_g":
        __UpperCamelCase					:List[str]        =		value
    elif weight_type == "weight_v":
        __UpperCamelCase					:List[Any]        =		value
    elif weight_type == "bias":
        __UpperCamelCase					:Any        =		value
    elif weight_type == "running_mean":
        __UpperCamelCase					:List[str]        =		value
    elif weight_type == "running_var":
        __UpperCamelCase					:Optional[int]        =		value
    elif weight_type == "num_batches_tracked":
        __UpperCamelCase					:Optional[int]        =		value
    else:
        __UpperCamelCase					:Union[str, Any]        =		value

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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    for key in ignore_keys:
        if key.endswith('''.*'''     ):
            if name.startswith(key[:-1]     ):
                return True
        elif ".*." in key:
            __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		key.split('''.*.'''     )
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		[]

    if task == "s2t":
        __UpperCamelCase					:Any        =		hf_model.speechta.encoder.prenet.feature_encoder
        __UpperCamelCase					:Any        =		MAPPING_S2T
        __UpperCamelCase					:str        =		IGNORE_KEYS_S2T
    elif task == "t2s":
        __UpperCamelCase					:str        =		None
        __UpperCamelCase					:str        =		MAPPING_T2S
        __UpperCamelCase					:Union[str, Any]        =		IGNORE_KEYS_T2S
    elif task == "s2s":
        __UpperCamelCase					:Dict        =		hf_model.speechta.encoder.prenet.feature_encoder
        __UpperCamelCase					:Optional[int]        =		MAPPING_S2S
        __UpperCamelCase					:Any        =		IGNORE_KEYS_S2S
    else:
        raise ValueError(f"""Unsupported task: {task}"""     )

    for name, value in fairseq_dict.items():
        if should_ignore(lowerCAmelCase__  ,      lowerCAmelCase__     ):
            logger.info(f"""{name} was ignored"""     )
            continue

        __UpperCamelCase					:Dict        =		False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__  ,      lowerCAmelCase__  ,      hf_model.config.feat_extract_norm == '''group'''  ,      )
            __UpperCamelCase					:List[Any]        =		True
        else:
            for key, mapped_key in MAPPING.items():
                # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                if "*" in key:
                    __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		key.split('''.*.'''     )
                    if prefix in name and suffix in name:
                        __UpperCamelCase					:Optional[Any]        =		suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    __UpperCamelCase					:List[str]        =		True
                    if "*" in mapped_key:
                        __UpperCamelCase					:List[Any]        =		name.split(lowerCAmelCase__     )[0].split('''.'''     )[-2]
                        __UpperCamelCase					:List[str]        =		mapped_key.replace('''*'''  ,      lowerCAmelCase__     )
                    if "weight_g" in name:
                        __UpperCamelCase					:Optional[Any]        =		'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase					:Any        =		'''weight_v'''
                    elif "bias" in name:
                        __UpperCamelCase					:Union[str, Any]        =		'''bias'''
                    elif "weight" in name:
                        __UpperCamelCase					:Tuple        =		'''weight'''
                    elif "running_mean" in name:
                        __UpperCamelCase					:Tuple        =		'''running_mean'''
                    elif "running_var" in name:
                        __UpperCamelCase					:Optional[int]        =		'''running_var'''
                    elif "num_batches_tracked" in name:
                        __UpperCamelCase					:List[Any]        =		'''num_batches_tracked'''
                    else:
                        __UpperCamelCase					:List[str]        =		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  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[Any]        =		full_name.split('''conv_layers.'''     )[-1]
    __UpperCamelCase					:List[Any]        =		name.split('''.'''     )
    __UpperCamelCase					:int        =		int(items[0]     )
    __UpperCamelCase					:Union[str, Any]        =		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."""     )
            __UpperCamelCase					:Tuple        =		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."""     )
            __UpperCamelCase					:List[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:
            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."""     )
            __UpperCamelCase					:Tuple        =		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."""     )
            __UpperCamelCase					:int        =		value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
    else:
        unused_weights.append(lowerCAmelCase__     )



@torch.no_grad()
def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      SCREAMING_SNAKE_CASE=None  ,      ):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase					:Optional[Any]        =		SpeechTaConfig.from_pretrained(lowerCAmelCase__     )
    else:
        __UpperCamelCase					:Tuple        =		SpeechTaConfig()

    if task == "s2t":
        __UpperCamelCase					:Any        =		config.max_text_positions
        __UpperCamelCase					:Any        =		SpeechTaForSpeechToText(lowerCAmelCase__     )
    elif task == "t2s":
        __UpperCamelCase					:List[str]        =		1_876
        __UpperCamelCase					:List[str]        =		600
        __UpperCamelCase					:Optional[Any]        =		config.max_speech_positions
        __UpperCamelCase					:Tuple        =		SpeechTaForTextToSpeech(lowerCAmelCase__     )
    elif task == "s2s":
        __UpperCamelCase					:Any        =		1_876
        __UpperCamelCase					:Tuple        =		config.max_speech_positions
        __UpperCamelCase					:Tuple        =		SpeechTaForSpeechToSpeech(lowerCAmelCase__     )
    else:
        raise ValueError(f"""Unknown task name: {task}"""     )

    if vocab_path:
        __UpperCamelCase					:List[str]        =		SpeechTaTokenizer(lowerCAmelCase__  ,      model_max_length=config.max_text_positions     )

        # Mask token behaves like a normal word, i.e. include the space before it
        __UpperCamelCase					:Dict        =		AddedToken('''<mask>'''  ,      lstrip=lowerCAmelCase__  ,      rstrip=lowerCAmelCase__     )
        __UpperCamelCase					:Optional[Any]        =		mask_token
        tokenizer.add_special_tokens({'''mask_token''': mask_token}     )
        tokenizer.add_tokens(['''<ctc_blank>''']     )

    __UpperCamelCase					:str        =		SpeechTaFeatureExtractor()
    __UpperCamelCase					:List[Any]        =		SpeechTaProcessor(tokenizer=lowerCAmelCase__  ,      feature_extractor=lowerCAmelCase__     )
    processor.save_pretrained(lowerCAmelCase__     )

    __UpperCamelCase					:List[str]        =		torch.load(lowerCAmelCase__     )
    recursively_load_weights(fairseq_checkpoint['''model''']  ,      lowerCAmelCase__  ,      lowerCAmelCase__     )

    model.save_pretrained(lowerCAmelCase__     )

    if repo_id:
        print('''Pushing to the hub...'''     )
        processor.push_to_hub(lowerCAmelCase__     )
        model.push_to_hub(lowerCAmelCase__     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    parser.add_argument(
        '''--task''',
        default='''s2t''',
        type=str,
        help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
    )
    parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )

    __lowercase										= parser.parse_args()
    convert_speechta_checkpoint(
        args.task,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.vocab_path,
        args.push_to_hub,
    )






import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


__lowercase										= logging.getLogger(__name__)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						str      =				"""token-classification"""







    def __init__(     self			,			__lowercase)	->     str:
        if type(__lowercase) == dict:
            __UpperCamelCase					:List[Any]        =		Namespace(**__lowercase)
        __UpperCamelCase					:Dict        =		import_module('''tasks''')
        try:
            __UpperCamelCase					:str        =		getattr(__lowercase			,			hparams.task_type)
            __UpperCamelCase					:TokenClassificationTask        =		token_classification_task_clazz()
        except AttributeError:
            raise ValueError(
                f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""")
        __UpperCamelCase					:Tuple        =		self.token_classification_task.get_labels(hparams.labels)
        __UpperCamelCase					:Tuple        =		CrossEntropyLoss().ignore_index
        super().__init__(__lowercase			,			len(self.labels)			,			self.mode)







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     List[Any]:
        return self.model(**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Any:
        __UpperCamelCase					:str        =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            __UpperCamelCase					:Union[str, Any]        =		(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids

        __UpperCamelCase					:Dict        =		self(**__lowercase)
        __UpperCamelCase					:str        =		outputs[0]
        # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss}







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		self.hparams
        for mode in ["train", "dev", "test"]:
            __UpperCamelCase					:int        =		self._feature_file(__lowercase)
            if os.path.exists(__lowercase) and not args.overwrite_cache:
                logger.info('''Loading features from cached file %s'''			,			__lowercase)
                __UpperCamelCase					:Any        =		torch.load(__lowercase)
            else:
                logger.info('''Creating features from dataset file at %s'''			,			args.data_dir)
                __UpperCamelCase					:Any        =		self.token_classification_task.read_examples_from_file(args.data_dir			,			__lowercase)
                __UpperCamelCase					:Union[str, Any]        =		self.token_classification_task.convert_examples_to_features(
                    __lowercase			,			self.labels			,			args.max_seq_length			,			self.tokenizer			,			cls_token_at_end=bool(self.config.model_type in ['''xlnet'''])			,			cls_token=self.tokenizer.cls_token			,			cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0			,			sep_token=self.tokenizer.sep_token			,			sep_token_extra=__lowercase			,			pad_on_left=bool(self.config.model_type in ['''xlnet'''])			,			pad_token=self.tokenizer.pad_token_id			,			pad_token_segment_id=self.tokenizer.pad_token_type_id			,			pad_token_label_id=self.pad_token_label_id			,			)
                logger.info('''Saving features into cached file %s'''			,			__lowercase)
                torch.save(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = False)	->     DataLoader:
        __UpperCamelCase					:Tuple        =		self._feature_file(__lowercase)
        logger.info('''Loading features from cached file %s'''			,			__lowercase)
        __UpperCamelCase					:str        =		torch.load(__lowercase)
        __UpperCamelCase					:int        =		torch.tensor([f.input_ids for f in features]			,			dtype=torch.long)
        __UpperCamelCase					:Optional[Any]        =		torch.tensor([f.attention_mask for f in features]			,			dtype=torch.long)
        if features[0].token_type_ids is not None:
            __UpperCamelCase					:str        =		torch.tensor([f.token_type_ids for f in features]			,			dtype=torch.long)
        else:
            __UpperCamelCase					:Union[str, Any]        =		torch.tensor([0 for f in features]			,			dtype=torch.long)
            # HACK(we will not use this anymore soon)
        __UpperCamelCase					:int        =		torch.tensor([f.label_ids for f in features]			,			dtype=torch.long)
        return DataLoader(
            TensorDataset(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)			,			batch_size=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase)	->     Dict:
        """Compute validation""" ""
        __UpperCamelCase					:int        =		{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
        if self.config.model_type != "distilbert":
            __UpperCamelCase					:Any        =		(
                batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
            )  # XLM and RoBERTa don"t use token_type_ids
        __UpperCamelCase					:Any        =		self(**__lowercase)
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		outputs[:2]
        __UpperCamelCase					:List[str]        =		logits.detach().cpu().numpy()
        __UpperCamelCase					:List[str]        =		inputs['''labels'''].detach().cpu().numpy()
        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Tuple        =		torch.stack([x['''val_loss'''] for x in outputs]).mean()
        __UpperCamelCase					:str        =		np.concatenate([x['''pred'''] for x in outputs]			,			axis=0)
        __UpperCamelCase					:Any        =		np.argmax(__lowercase			,			axis=2)
        __UpperCamelCase					:str        =		np.concatenate([x['''target'''] for x in outputs]			,			axis=0)

        __UpperCamelCase					:List[str]        =		dict(enumerate(self.labels))
        __UpperCamelCase					:Tuple        =		[[] for _ in range(out_label_ids.shape[0])]
        __UpperCamelCase					:Any        =		[[] for _ in range(out_label_ids.shape[0])]

        for i in range(out_label_ids.shape[0]):
            for j in range(out_label_ids.shape[1]):
                if out_label_ids[i, j] != self.pad_token_label_id:
                    out_label_list[i].append(label_map[out_label_ids[i][j]])
                    preds_list[i].append(label_map[preds[i][j]])

        __UpperCamelCase					:Any        =		{
            '''val_loss''': val_loss_mean,
            '''accuracy_score''': accuracy_score(__lowercase			,			__lowercase),
            '''precision''': precision_score(__lowercase			,			__lowercase),
            '''recall''': recall_score(__lowercase			,			__lowercase),
            '''f1''': fa_score(__lowercase			,			__lowercase),
        }

        __UpperCamelCase					:Dict        =		dict(results.items())
        __UpperCamelCase					:List[str]        =		results
        return ret, preds_list, out_label_list







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        # when stable
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._eval_end(__lowercase)
        __UpperCamelCase					:Tuple        =		ret['''log''']
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        # updating to test_epoch_end instead of deprecated test_end
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		self._eval_end(__lowercase)

        # Converting to the dict required by pl
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
        # pytorch_lightning/trainer/logging.py#L139
        __UpperCamelCase					:Optional[Any]        =		ret['''log''']
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    @staticmethod
    def       UpperCamelCase__       (     __lowercase			,			__lowercase)	->     Union[str, Any]:
        # Add NER specific options
        BaseTransformer.add_model_specific_args(__lowercase			,			__lowercase)
        parser.add_argument(
            '''--task_type'''			,			default='''NER'''			,			type=__lowercase			,			help='''Task type to fine tune in training (e.g. NER, POS, etc)''')
        parser.add_argument(
            '''--max_seq_length'''			,			default=128			,			type=__lowercase			,			help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )			,			)

        parser.add_argument(
            '''--labels'''			,			default=''''''			,			type=__lowercase			,			help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''			,			)
        parser.add_argument(
            '''--gpus'''			,			default=0			,			type=__lowercase			,			help='''The number of GPUs allocated for this, it is by default 0 meaning none'''			,			)

        parser.add_argument(
            '''--overwrite_cache'''			,			action='''store_true'''			,			help='''Overwrite the cached training and evaluation sets''')

        return parser


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    __lowercase										= NERTransformer.add_model_specific_args(parser, os.getcwd())
    __lowercase										= parser.parse_args()
    __lowercase										= NERTransformer(args)
    __lowercase										= generic_train(model, args)

    if args.do_predict:
        # See https://github.com/huggingface/transformers/issues/3159
        # pl use this default format to create a checkpoint:
        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
        # /pytorch_lightning/callbacks/model_checkpoint.py#L322
        __lowercase										= sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
        __lowercase										= model.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)





