import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
     import torch







@require_flax
class      SCREAMING_SNAKE_CASE__					:

       def __init__(							self					:     Dict			,			_lowerCAmelCase					:     List[str]			,			_lowerCAmelCase					:     Any=14			,			_lowerCAmelCase					:     int=7			,			_lowerCAmelCase					:     List[str]=True			,			_lowerCAmelCase					:     Optional[int]=True			,			_lowerCAmelCase					:     Dict=True			,			_lowerCAmelCase					:     Tuple=99			,			_lowerCAmelCase					:     str=32			,			_lowerCAmelCase					:     int=5			,			_lowerCAmelCase					:     int=4			,			_lowerCAmelCase					:     Dict=37			,			_lowerCAmelCase					:     List[str]="gelu"			,			_lowerCAmelCase					:     str=0.1			,			_lowerCAmelCase					:     Union[str, Any]=0.1			,			_lowerCAmelCase					:     Optional[int]=5_12			,			_lowerCAmelCase					:     List[Any]=0.02			,			_lowerCAmelCase					:     Union[str, Any]=None			,			):
           __snake_case       :				Tuple 			=    parent
           __snake_case       :				Any 			=    batch_size
           __snake_case       :				List[str] 			=    seq_length
           __snake_case       :				Tuple 			=    is_training
           __snake_case       :				List[str] 			=    use_input_mask
           __snake_case       :				Union[str, Any] 			=    use_labels
           __snake_case       :				Union[str, Any] 			=    vocab_size
           __snake_case       :				Optional[int] 			=    d_model
           __snake_case       :				Dict 			=    num_hidden_layers
           __snake_case       :				Optional[Any] 			=    num_attention_heads
           __snake_case       :				Optional[int] 			=    ffn_dim
           __snake_case       :				int 			=    activation_function
           __snake_case       :				List[str] 			=    activation_dropout
           __snake_case       :				List[str] 			=    attention_dropout
           __snake_case       :				Tuple 			=    max_position_embeddings
           __snake_case       :				Union[str, Any] 			=    initializer_range
           __snake_case       :				Optional[int] 			=    None
           __snake_case       :				int 			=    0
           __snake_case       :				List[Any] 			=    2
           __snake_case       :				Dict 			=    1

       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				Optional[Any] 			=    np.clip(ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size			)			,			3			,			self.vocab_size			)

           __snake_case       :				Union[str, Any] 			=    None
           if self.use_input_mask:
               __snake_case       :				str 			=    random_attention_mask([self.batch_size, self.seq_length]			)

           __snake_case       :				List[str] 			=    XGLMConfig(
               vocab_size=self.vocab_size			,			d_model=self.hidden_size			,			num_layers=self.num_hidden_layers			,			attention_heads=self.num_attention_heads			,			ffn_dim=self.ffn_dim			,			activation_function=self.activation_function			,			activation_dropout=self.activation_dropout			,			attention_dropout=self.attention_dropout			,			max_position_embeddings=self.max_position_embeddings			,			initializer_range=self.initializer_range			,			use_cache=_lowerCAmelCase			,			bos_token_id=self.bos_token_id			,			eos_token_id=self.eos_token_id			,			pad_token_id=self.pad_token_id			,			)

           return (config, input_ids, input_mask)

       def 			snake_case__    (							self					:     Any			):
           __snake_case       :				Union[str, Any] 			=    self.prepare_config_and_inputs()
           __snake_case			, __snake_case			, __snake_case       :				Optional[Any] 			=    config_and_inputs
           __snake_case       :				int 			=    {"""input_ids""": input_ids, """attention_mask""": attention_mask}
           return config, inputs_dict

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case			, __snake_case			, __snake_case       :				Dict 			=    self.prepare_config_and_inputs()

           __snake_case       :				int 			=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
           __snake_case       :				Optional[int] 			=    ids_tensor([self.batch_size, self.seq_length]			,			vocab_size=2			)

           return (
               config,
               input_ids,
               attention_mask,
               encoder_hidden_states,
               encoder_attention_mask,
           )

       def 			snake_case__    (							self					:     List[Any]			,			_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Any			,			_lowerCAmelCase					:     Dict			):
           __snake_case       :				List[Any] 			=    20
           __snake_case       :				Optional[Any] 			=    model_class_name(_lowerCAmelCase			)

           __snake_case       :				int 			=    model.init_cache(input_ids.shape[0]			,			_lowerCAmelCase			)
           __snake_case       :				str 			=    jnp.ones((input_ids.shape[0], max_decoder_length)			,			dtype="""i4"""			)

           __snake_case       :				List[Any] 			=    jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1			)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)
           __snake_case       :				Union[str, Any] 			=    model(
               input_ids[:, :-1]			,			attention_mask=_lowerCAmelCase			,			past_key_values=_lowerCAmelCase			,			position_ids=_lowerCAmelCase			,			)

           __snake_case       :				str 			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype="""i4"""			)
           __snake_case       :				Optional[int] 			=    model(
               input_ids[:, -1:]			,			attention_mask=_lowerCAmelCase			,			past_key_values=outputs_cache.past_key_values			,			position_ids=_lowerCAmelCase			,			)

           __snake_case       :				Optional[int] 			=    model(_lowerCAmelCase			)

           __snake_case       :				List[Any] 			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
           self.parent.assertTrue(diff < 1e-3			,			msg=f'''Max diff is {diff}'''			)





       def 			snake_case__    (							self					:     Dict			,			_lowerCAmelCase					:     Dict			,			_lowerCAmelCase					:     List[Any]			,			_lowerCAmelCase					:     Optional[Any]			,			_lowerCAmelCase					:     Optional[int]			):
           __snake_case       :				Union[str, Any] 			=    20
           __snake_case       :				Any 			=    model_class_name(_lowerCAmelCase			)

           __snake_case       :				Any 			=    jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])			)]			,			axis=-1			,			)

           __snake_case       :				int 			=    model.init_cache(input_ids.shape[0]			,			_lowerCAmelCase			)
           __snake_case       :				Union[str, Any] 			=    jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1			)[None, :]			,			(input_ids.shape[0], input_ids.shape[-1] - 1)			)

           __snake_case       :				str 			=    model(
               input_ids[:, :-1]			,			attention_mask=_lowerCAmelCase			,			past_key_values=_lowerCAmelCase			,			position_ids=_lowerCAmelCase			,			)
           __snake_case       :				Union[str, Any] 			=    jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,			dtype="""i4"""			)
           __snake_case       :				Tuple 			=    model(
               input_ids[:, -1:]			,			past_key_values=outputs_cache.past_key_values			,			attention_mask=_lowerCAmelCase			,			position_ids=_lowerCAmelCase			,			)

           __snake_case       :				Any 			=    model(_lowerCAmelCase			,			attention_mask=_lowerCAmelCase			)
           __snake_case       :				str 			=    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
           self.parent.assertTrue(diff < 1e-3			,			msg=f'''Max diff is {diff}'''			)







@require_sentencepiece
@require_flax
class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase   ,							__UpperCamelCase   ,							unittest.TestCase    ):
       A							:	List[str]          =	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       A							:	int          =	(FlaxXGLMForCausalLM,) if is_flax_available() else ()

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Union[str, Any] 			=    FlaxXGLMModelTester(self			)

       def 			snake_case__    (							self					:     Any			):
           for model_class_name in self.all_model_classes:
               __snake_case			, __snake_case			, __snake_case       :				Union[str, Any] 			=    self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward(_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			)

       def 			snake_case__    (							self					:     int			):
           for model_class_name in self.all_model_classes:
               __snake_case			, __snake_case			, __snake_case       :				Optional[Any] 			=    self.model_tester.prepare_config_and_inputs()
               self.model_tester.check_use_cache_forward_with_attn_mask(
                   _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase			)

       @slow
       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Optional[Any] 			=    XGLMTokenizer.from_pretrained("""XGLM"""			,			padding_side="""left"""			)
           __snake_case       :				List[str] 			=    tokenizer(["""Hello this is a long string""", """Hey"""]			,			return_tensors="""np"""			,			padding=_lowerCAmelCase			,			truncation=_lowerCAmelCase			)

           __snake_case       :				Optional[int] 			=    FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
           __snake_case       :				Union[str, Any] 			=    1
           __snake_case       :				int 			=    False

           __snake_case       :				Optional[int] 			=    jax.jit(model.generate			)

           __snake_case       :				str 			=    jit_generate(inputs["""input_ids"""]			,			attention_mask=inputs["""attention_mask"""]			).sequences

           __snake_case       :				Dict 			=    tokenizer.batch_decode(_lowerCAmelCase			,			skip_special_tokens=_lowerCAmelCase			)

           __snake_case       :				Dict 			=    [
               """Hello this is a long string of questions, but I'm not sure if I'm""",
               """Hey, I'm a newbie to the forum and I'""",
           ]

           self.assertListEqual(_lowerCAmelCase			,			_lowerCAmelCase			)

       @is_pt_flax_cross_test
       def 			snake_case__    (							self					:     int			):
           __snake_case			, __snake_case       :				Union[str, Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                   # prepare inputs
                   __snake_case       :				str 			=    self._prepare_for_class(_lowerCAmelCase			,			_lowerCAmelCase			)
                   __snake_case       :				List[Any] 			=    {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   __snake_case       :				Optional[Any] 			=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   __snake_case       :				int 			=    getattr(_lowerCAmelCase			,			_lowerCAmelCase			)

                   __snake_case			, __snake_case       :				Any 			=    pt_inputs["""input_ids"""].shape
                   __snake_case       :				Any 			=    np.random.randint(0			,			seq_length - 1			,			size=(batch_size,)			)
                   for batch_idx, start_index in enumerate(_lowerCAmelCase			):
                       __snake_case       :				List[str] 			=    0
                       __snake_case       :				Union[str, Any] 			=    1
                       __snake_case       :				Optional[int] 			=    0
                       __snake_case       :				Any 			=    1
                   __snake_case       :				Optional[Any] 			=    pt_model_class(_lowerCAmelCase			).eval()
                   # Flax models don't use the `use_cache` option and cache is not returned as a default.
                   # So we disable `use_cache` here for PyTorch model.
                   __snake_case       :				Optional[int] 			=    False
                   __snake_case       :				Tuple 			=    model_class(_lowerCAmelCase			,			dtype=jnp.floataa			)
                   __snake_case       :				int 			=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,			_lowerCAmelCase			)
                   __snake_case       :				Dict 			=    fx_state

                   with torch.no_grad():
                       __snake_case       :				str 			=    pt_model(**_lowerCAmelCase			).to_tuple()

                   __snake_case       :				int 			=    fx_model(**_lowerCAmelCase			).to_tuple()
                   self.assertEqual(len(_lowerCAmelCase			)			,			len(_lowerCAmelCase			)			,			"""Output lengths differ between Flax and PyTorch"""			)
                   for fx_output, pt_output in zip(_lowerCAmelCase			,			_lowerCAmelCase			):
                       self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4e-2			)

                   with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(_lowerCAmelCase			)
                       __snake_case       :				Any 			=    model_class.from_pretrained(_lowerCAmelCase			,			from_pt=_lowerCAmelCase			)

                   __snake_case       :				Optional[int] 			=    fx_model_loaded(**_lowerCAmelCase			).to_tuple()
                   self.assertEqual(
                       len(_lowerCAmelCase			)			,			len(_lowerCAmelCase			)			,			"""Output lengths differ between Flax and PyTorch"""			)
                   for fx_output_loaded, pt_output in zip(_lowerCAmelCase			,			_lowerCAmelCase			):
                       self.assert_almost_equals(fx_output_loaded[:, -1]			,			pt_output[:, -1].numpy()			,			4e-2			)

       @is_pt_flax_cross_test
       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case			, __snake_case       :				List[Any] 			=    self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__			):
                   # prepare inputs
                   __snake_case       :				Optional[int] 			=    self._prepare_for_class(_lowerCAmelCase			,			_lowerCAmelCase			)
                   __snake_case       :				Any 			=    {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   __snake_case       :				Tuple 			=    model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   __snake_case       :				Union[str, Any] 			=    getattr(_lowerCAmelCase			,			_lowerCAmelCase			)

                   __snake_case       :				List[Any] 			=    pt_model_class(_lowerCAmelCase			).eval()
                   __snake_case       :				int 			=    False
                   __snake_case       :				Optional[int] 			=    model_class(_lowerCAmelCase			,			dtype=jnp.floataa			)

                   __snake_case       :				Optional[int] 			=    load_flax_weights_in_pytorch_model(_lowerCAmelCase			,			fx_model.params			)
                   __snake_case			, __snake_case       :				int 			=    pt_inputs["""input_ids"""].shape
                   __snake_case       :				Dict 			=    np.random.randint(0			,			seq_length - 1			,			size=(batch_size,)			)
                   for batch_idx, start_index in enumerate(_lowerCAmelCase			):
                       __snake_case       :				Tuple 			=    0
                       __snake_case       :				List[str] 			=    1
                       __snake_case       :				Optional[int] 			=    0
                       __snake_case       :				List[str] 			=    1

                   # make sure weights are tied in PyTorch
                   pt_model.tie_weights()

                   with torch.no_grad():
                       __snake_case       :				Dict 			=    pt_model(**_lowerCAmelCase			).to_tuple()

                   __snake_case       :				Union[str, Any] 			=    fx_model(**_lowerCAmelCase			).to_tuple()
                   self.assertEqual(len(_lowerCAmelCase			)			,			len(_lowerCAmelCase			)			,			"""Output lengths differ between Flax and PyTorch"""			)
                   for fx_output, pt_output in zip(_lowerCAmelCase			,			_lowerCAmelCase			):
                       self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4e-2			)

                   with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(_lowerCAmelCase			)
                       __snake_case       :				Union[str, Any] 			=    pt_model_class.from_pretrained(_lowerCAmelCase			,			from_flax=_lowerCAmelCase			)

                   with torch.no_grad():
                       __snake_case       :				int 			=    pt_model_loaded(**_lowerCAmelCase			).to_tuple()

                   self.assertEqual(
                       len(_lowerCAmelCase			)			,			len(_lowerCAmelCase			)			,			"""Output lengths differ between Flax and PyTorch"""			)
                   for fx_output, pt_output in zip(_lowerCAmelCase			,			_lowerCAmelCase			):
                       self.assert_almost_equals(fx_output[:, -1]			,			pt_output[:, -1].numpy()			,			4e-2			)





       @slow
       def 			snake_case__    (							self					:     Tuple			):
           for model_class_name in self.all_model_classes:
               __snake_case       :				Any 			=    model_class_name.from_pretrained("""facebook/xglm-564M"""			)
               __snake_case       :				Dict 			=    model(np.ones((1, 1)			)			)
               self.assertIsNotNone(_lowerCAmelCase			)




import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


lowercase_  							=			logging.getLogger(__name__)







@dataclass
class      SCREAMING_SNAKE_CASE__					:
       A							:	str          =	field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
       A							:	Optional[str]          =	field(
           default="NER"   ,							metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}    )
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
       A							:	bool          =	field(default=__UpperCamelCase   ,							metadata={"help": "Set this flag to use fast tokenization."}    )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       A							:	Optional[str]          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,							)







@dataclass
class      SCREAMING_SNAKE_CASE__					:
       A							:	str          =	field(
           metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}    )
       A							:	Optional[str]          =	field(
           metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}    )
       A							:	int          =	field(
           default=128   ,							metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }   ,							)
       A							:	bool          =	field(
           default=__UpperCamelCase   ,							metadata={"help": "Overwrite the cached training and evaluation sets"}    )







def 					__lowerCAmelCase				(		):







    '''simple docstring'''
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    __snake_case       :				Tuple 			=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
    __snake_case			, __snake_case			, __snake_case       :				Tuple 			=    parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir   )
        and os.listdir(training_args.output_dir   )
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
            """ --overwrite_output_dir to overcome."""   )

    __snake_case       :				int 			=    import_module("""tasks"""   )

    try:
        __snake_case       :				Union[str, Any] 			=    getattr(__SCREAMING_SNAKE_CASE		,							model_args.task_type   )
        __snake_case       :				TokenClassificationTask 			=    token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
            F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''   )

    # Setup logging
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,							datefmt="""%m/%d/%Y %H:%M:%S"""		,							level=logging.INFO		,							)
    logger.info(
        """n_replicas: %s, distributed training: %s, 16-bits training: %s"""		,							training_args.n_replicas		,							bool(training_args.n_replicas > 1   )		,							training_args.fpaa		,							)
    logger.info("""Training/evaluation parameters %s"""		,							__SCREAMING_SNAKE_CASE   )

    # Prepare Token Classification task
    __snake_case       :				str 			=    token_classification_task.get_labels(data_args.labels   )
    __snake_case       :				Dict[int, str] 			=    dict(enumerate(__SCREAMING_SNAKE_CASE   )   )
    __snake_case       :				Any 			=    len(__SCREAMING_SNAKE_CASE   )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    __snake_case       :				List[str] 			=    AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path		,							num_labels=__SCREAMING_SNAKE_CASE		,							idalabel=__SCREAMING_SNAKE_CASE		,							labelaid={label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE   )}		,							cache_dir=model_args.cache_dir		,							)
    __snake_case       :				List[str] 			=    AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,							cache_dir=model_args.cache_dir		,							use_fast=model_args.use_fast		,							)

    with training_args.strategy.scope():
        __snake_case       :				Optional[int] 			=    TFAutoModelForTokenClassification.from_pretrained(
            model_args.model_name_or_path		,							from_pt=bool(""".bin""" in model_args.model_name_or_path   )		,							config=__SCREAMING_SNAKE_CASE		,							cache_dir=model_args.cache_dir		,							)

    # Get datasets
    __snake_case       :				Optional[int] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.train		,							)
        if training_args.do_train
        else None
    )
    __snake_case       :				Union[str, Any] 			=    (
        TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.dev		,							)
        if training_args.do_eval
        else None
    )

    def align_predictions(__SCREAMING_SNAKE_CASE       :					np.ndarray		,							__SCREAMING_SNAKE_CASE       :					np.ndarray   ) -> Tuple[List[int], List[int]]:
        __snake_case       :				Dict 			=    np.argmax(__SCREAMING_SNAKE_CASE		,							axis=2   )
        __snake_case			, __snake_case       :				int 			=    preds.shape
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]
        __snake_case       :				Union[str, Any] 			=    [[] for _ in range(__SCREAMING_SNAKE_CASE   )]

        for i in range(__SCREAMING_SNAKE_CASE   ):
            for j in range(__SCREAMING_SNAKE_CASE   ):
                if label_ids[i, j] != -1_0_0:
                    out_label_list[i].append(label_map[label_ids[i][j]]   )
                    preds_list[i].append(label_map[preds[i][j]]   )

        return preds_list, out_label_list

    def compute_metrics(__SCREAMING_SNAKE_CASE       :					EvalPrediction   ) -> Dict:
        __snake_case			, __snake_case       :				Optional[int] 			=    align_predictions(p.predictions		,							p.label_ids   )

        return {
            "precision": precision_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
            "recall": recall_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
            "f1": fa_score(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   ),
        }

    # Initialize our Trainer
    __snake_case       :				Optional[Any] 			=    TFTrainer(
        model=__SCREAMING_SNAKE_CASE		,							args=__SCREAMING_SNAKE_CASE		,							train_dataset=train_dataset.get_dataset() if train_dataset else None		,							eval_dataset=eval_dataset.get_dataset() if eval_dataset else None		,							compute_metrics=__SCREAMING_SNAKE_CASE		,							)

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
    __snake_case       :				Tuple 			=    {}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""   )

        __snake_case       :				List[str] 			=    trainer.evaluate()
        __snake_case       :				Dict 			=    os.path.join(training_args.output_dir		,							"""eval_results.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            logger.info("""***** Eval results *****"""   )

            for key, value in result.items():
                logger.info("""  %s = %s"""		,							__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
                writer.write("""%s = %s\n""" % (key, value)   )

            results.update(__SCREAMING_SNAKE_CASE   )

    # Predict
    if training_args.do_predict:
        __snake_case       :				Any 			=    TFTokenClassificationDataset(
            token_classification_task=__SCREAMING_SNAKE_CASE		,							data_dir=data_args.data_dir		,							tokenizer=__SCREAMING_SNAKE_CASE		,							labels=__SCREAMING_SNAKE_CASE		,							model_type=config.model_type		,							max_seq_length=data_args.max_seq_length		,							overwrite_cache=data_args.overwrite_cache		,							mode=Split.test		,							)

        __snake_case			, __snake_case			, __snake_case       :				Optional[Any] 			=    trainer.predict(test_dataset.get_dataset()   )
        __snake_case			, __snake_case       :				Dict 			=    align_predictions(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )
        __snake_case       :				Optional[Any] 			=    classification_report(__SCREAMING_SNAKE_CASE		,							__SCREAMING_SNAKE_CASE   )

        logger.info("""\n%s"""		,							__SCREAMING_SNAKE_CASE   )

        __snake_case       :				Optional[int] 			=    os.path.join(training_args.output_dir		,							"""test_results.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            writer.write("""%s\n""" % report   )

        # Save predictions
        __snake_case       :				Dict 			=    os.path.join(training_args.output_dir		,							"""test_predictions.txt"""   )

        with open(__SCREAMING_SNAKE_CASE		,							"""w"""   ) as writer:
            with open(os.path.join(data_args.data_dir		,							"""test.txt"""   )		,							"""r"""   ) as f:
                __snake_case       :				List[str] 			=    0

                for line in f:
                    if line.startswith("""-DOCSTART-"""   ) or line == "" or line == "\n":
                        writer.write(__SCREAMING_SNAKE_CASE   )

                        if not preds_list[example_id]:
                            example_id += 1
                    elif preds_list[example_id]:
                        __snake_case       :				List[Any] 			=    line.split()[0] + """ """ + preds_list[example_id].pop(0   ) + """\n"""

                        writer.write(__SCREAMING_SNAKE_CASE   )
                    else:
                        logger.warning("""Maximum sequence length exceeded: No prediction for '%s'."""		,							line.split()[0]   )

    return results


if __name__ == "__main__":
     main()



