
'''simple docstring'''



import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RwkvForCausalLM,
				    RwkvModel,
				)
				from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
				__A       =      False





class      A							:



			def __init__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=14    ,       lowerCamelCase__=7    ,       lowerCamelCase__=True    ,       lowerCamelCase__=False    ,       lowerCamelCase__=True    ,       lowerCamelCase__=True    ,       lowerCamelCase__=True    ,       lowerCamelCase__=99    ,       lowerCamelCase__=32    ,       lowerCamelCase__=5    ,       lowerCamelCase__=37    ,       lowerCamelCase__="gelu"    ,       lowerCamelCase__=0.1    ,       lowerCamelCase__=0.1    ,       lowerCamelCase__=512    ,       lowerCamelCase__=16    ,       lowerCamelCase__=2    ,       lowerCamelCase__=3    ,       lowerCamelCase__=4    ,       lowerCamelCase__=None    ,       )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    parent
									lowercase__								=    batch_size
									lowercase__								=    seq_length
									lowercase__								=    is_training
									lowercase__								=    use_token_type_ids
									lowercase__								=    use_input_mask
									lowercase__								=    use_labels
									lowercase__								=    use_mc_token_ids
									lowercase__								=    vocab_size
									lowercase__								=    hidden_size
									lowercase__								=    num_hidden_layers
									lowercase__								=    intermediate_size
									lowercase__								=    hidden_act
									lowercase__								=    hidden_dropout_prob
									lowercase__								=    attention_probs_dropout_prob
									lowercase__								=    max_position_embeddings
									lowercase__								=    type_vocab_size
									lowercase__								=    type_sequence_label_size
									lowercase__								=    num_labels
									lowercase__								=    num_choices
									lowercase__								=    scope
									lowercase__								=    vocab_size - 1
									lowercase__								=    vocab_size - 1
									lowercase__								=    vocab_size - 1



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									return RwkvConfig.from_pretrained("""sgugger/rwkv-4-pile-7b"""    )



			def A__	(   self    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    )     ->						str:




									'''simple docstring'''



									lowercase__								=    ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size    )

									lowercase__								=    None
									if self.use_input_mask:
															lowercase__								=    random_attention_mask([self.batch_size, self.seq_length]    )

									lowercase__								=    None
									if self.use_token_type_ids:
															lowercase__								=    ids_tensor([self.batch_size, self.seq_length]    ,       self.type_vocab_size    )

									lowercase__								=    None
									if self.use_mc_token_ids:
															lowercase__								=    ids_tensor([self.batch_size, self.num_choices]    ,       self.seq_length    )

									lowercase__								=    None
									lowercase__								=    None
									lowercase__								=    None
									if self.use_labels:
															lowercase__								=    ids_tensor([self.batch_size]    ,       self.type_sequence_label_size    )
															lowercase__								=    ids_tensor([self.batch_size, self.seq_length]    ,       self.num_labels    )
															lowercase__								=    ids_tensor([self.batch_size]    ,       self.num_choices    )

									lowercase__								=    self.get_config(
									    gradient_checkpointing=lowerCamelCase__    ,       scale_attn_by_inverse_layer_idx=lowerCamelCase__    ,       reorder_and_upcast_attn=lowerCamelCase__    ,       )

									return (
									    config,
									    input_ids,
									    input_mask,
									    None,
									    token_type_ids,
									    mc_token_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									)



			def A__	(   self    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    ,       lowerCamelCase__=False    )     ->						Dict:




									'''simple docstring'''



									return RwkvConfig(
									    vocab_size=self.vocab_size    ,       hidden_size=self.hidden_size    ,       num_hidden_layers=self.num_hidden_layers    ,       intermediate_size=self.intermediate_size    ,       activation_function=self.hidden_act    ,       resid_pdrop=self.hidden_dropout_prob    ,       attn_pdrop=self.attention_probs_dropout_prob    ,       n_positions=self.max_position_embeddings    ,       type_vocab_size=self.type_vocab_size    ,       use_cache=lowerCamelCase__    ,       bos_token_id=self.bos_token_id    ,       eos_token_id=self.eos_token_id    ,       pad_token_id=self.pad_token_id    ,       gradient_checkpointing=lowerCamelCase__    ,       scale_attn_by_inverse_layer_idx=lowerCamelCase__    ,       reorder_and_upcast_attn=lowerCamelCase__    ,       )



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    self.get_config()
									lowercase__								=    300
									return config



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									(
									    (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  
									)								=    self.prepare_config_and_inputs()

									lowercase__								=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
									lowercase__								=    ids_tensor([self.batch_size, self.seq_length]    ,       vocab_size=2    )

									return (
									    config,
									    input_ids,
									    input_mask,
									    head_mask,
									    token_type_ids,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       *lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    True
									lowercase__								=    RwkvModel(config=lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()

									lowercase__								=    model(lowerCamelCase__    )

									self.parent.assertEqual(result.last_hidden_state.shape    ,       (self.batch_size, self.seq_length, self.hidden_size)    )
									self.parent.assertEqual(len(result.hidden_states    )    ,       config.num_hidden_layers + 1    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       *lowerCamelCase__    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    RwkvForCausalLM(lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()

									lowercase__								=    model(lowerCamelCase__    ,       labels=lowerCamelCase__    )
									self.parent.assertEqual(result.loss.shape    ,       ()    )
									self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.seq_length, self.vocab_size)    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       *lowerCamelCase__    )     ->						str:




									'''simple docstring'''



									lowercase__								=    RwkvModel(config=lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()

									lowercase__								=    model(lowerCamelCase__    )
									lowercase__								=    outputs.last_hidden_state

									lowercase__								=    model(input_ids[:, :2]    )
									lowercase__								=    outputs.last_hidden_state

									# Using the state computed on the first inputs, we will get the same output
									lowercase__								=    model(input_ids[:, 2:]    ,       state=outputs.state    )
									lowercase__								=    outputs.last_hidden_state

									self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]    ,       dim=1    )    ,       lowerCamelCase__    ,       atol=1e-5    )    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       *lowerCamelCase__    ,       lowerCamelCase__=False    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    RwkvForCausalLM(lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									if gradient_checkpointing:
															model.gradient_checkpointing_enable()

									lowercase__								=    model(lowerCamelCase__    ,       labels=lowerCamelCase__    )
									self.parent.assertEqual(result.loss.shape    ,       ()    )
									self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.seq_length, self.vocab_size)    )
									result.loss.backward()



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    self.prepare_config_and_inputs()

									(
									    (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  (
									    lowercase__
									)	,  
									)								=    config_and_inputs

									lowercase__								=    {"""input_ids""": input_ids}

									return config, inputs_dict





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0						,  reason="""See https://github.com/huggingface/transformers/pull/24204"""			)
@require_torch
class      A							(    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Optional[Any]    						=					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
			lowerCamelCase							:							List[str]    						=					(
			    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
			)
			# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
			lowerCamelCase							:							int    						=					False
			lowerCamelCase							:							List[Any]    						=					False
			lowerCamelCase							:							str    						=					False
			lowerCamelCase							:							List[str]    						=					False
			lowerCamelCase							:							Optional[int]    						=					False  # Rwkv does not support head masking



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    RwkvModelTester(self    )
									lowercase__								=    ConfigTester(
									    self    ,       config_class=lowerCamelCase__    ,       n_embd=37    ,       common_properties=["""hidden_size""", """num_hidden_layers"""]    )



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__=None    )     ->						Optional[Any]:




									'''simple docstring'''



									if isinstance(lowerCamelCase__    ,       torch.Tensor    ):
															lowercase__	,  lowercase__								=    member.max().item(), member.min().item()
									elif isinstance(lowerCamelCase__    ,       lowerCamelCase__    ) or isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															lowercase__	,  lowercase__								=    max(lowerCamelCase__    ), min(lowerCamelCase__    )

									if not isinstance(lowerCamelCase__    ,       lowerCamelCase__    ):
															raise TypeError("""container should be a list or tuple"""    )
									elif len(lowerCamelCase__    ) != 2:
															raise ValueError("""container should have 2 elements"""    )

									lowercase__	,  lowercase__								=    container

									lowercase__								=    (min_value >= expected_min) and (max_value <= expected_max)

									if not is_inside_interval:
															lowercase__								=    """%s not found in %s""" % (safe_repr(lowerCamelCase__    ), safe_repr(lowerCamelCase__    ))
															self.fail(self._formatMessage(lowerCamelCase__    ,       lowerCamelCase__    )    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									self.config_tester.run_common_tests()



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_rwkv_model(*lowerCamelCase__    )



			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									lowercase__								=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_causl_lm(*lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_state_equivalency(*lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															lowercase__								=    model_class(config=lowerCamelCase__    )
															for name, param in model.named_parameters():
																					if "time_decay" in name:
																											if param.requires_grad:
																																	self.assertTrue(param.data.max().item() == 3.0    )
																																	self.assertTrue(param.data.min().item() == -5.0    )
																					elif "time_first" in name:
																											if param.requires_grad:
																																	# check if it's a ones like
																																	self.assertTrue(torch.allclose(param.data    ,       torch.ones_like(param.data    )    ,       atol=1e-5    ,       rtol=1e-5    )    )
																					elif any(x in name for x in ["""time_mix_key""", """time_mix_receptance"""]    ):
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data    ,       [0.0, 1.0]    ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,       )
																					elif "time_mix_value" in name:
																											if param.requires_grad:
																																	self.assertInterval(
																																	    param.data    ,       [0.0, 1.3]    ,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''    ,       )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__	,  lowercase__								=    self.model_tester.prepare_config_and_inputs_for_common()
									lowercase__								=    True

									lowercase__								=    getattr(self.model_tester    ,       """seq_length"""    ,       lowerCamelCase__    )

									for model_class in self.all_model_classes:
															lowercase__								=    True
															lowercase__								=    False
															lowercase__								=    True
															lowercase__								=    model_class(lowerCamelCase__    )
															model.to(lowerCamelCase__    )
															model.eval()

															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    inputs["""input_ids"""].shape[0]
															with torch.no_grad():
																					lowercase__								=    model(**lowerCamelCase__    )
															lowercase__								=    outputs.attentions
															self.assertEqual(len(lowerCamelCase__    )    ,       self.model_tester.num_hidden_layers    )

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															lowercase__								=    True
															lowercase__								=    model_class(lowerCamelCase__    )
															model.to(lowerCamelCase__    )
															model.eval()

															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    inputs["""input_ids"""].shape[0]
															with torch.no_grad():
																					lowercase__								=    model(**lowerCamelCase__    )
															lowercase__								=    outputs.attentions
															self.assertEqual(len(lowerCamelCase__    )    ,       self.model_tester.num_hidden_layers    )

															self.assertListEqual(
															    list(attentions[0].shape[-3:]    )    ,       [batch_size, seq_len, config.hidden_size]    ,       )
															lowercase__								=    len(lowerCamelCase__    )

															# Check attention is always last and order is fine
															lowercase__								=    True
															lowercase__								=    True
															lowercase__								=    model_class(lowerCamelCase__    )
															model.to(lowerCamelCase__    )
															model.eval()

															lowercase__								=    self._prepare_for_class(lowerCamelCase__    ,       lowerCamelCase__    )
															lowercase__								=    inputs["""input_ids"""].shape[0]
															with torch.no_grad():
																					lowercase__								=    model(**lowerCamelCase__    )

															lowercase__								=    1
															self.assertEqual(out_len + added_hidden_states    ,       len(lowerCamelCase__    )    )

															lowercase__								=    outputs.attentions

															self.assertEqual(len(lowerCamelCase__    )    ,       self.model_tester.num_hidden_layers    )
															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]    )    ,       [batch_size, seq_len, config.hidden_size]    ,       )



			@slow
			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase__								=    RwkvModel.from_pretrained(lowerCamelCase__    )
															self.assertIsNotNone(lowerCamelCase__    )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0						,  reason="""See https://github.com/huggingface/transformers/pull/24204"""			)
@slow
class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    """RWKV/rwkv-4-169m-pile"""
									lowercase__								=    AutoTokenizer.from_pretrained(self.model_id    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    """Hello my name is Jasmine and I am a newbie to the"""
									lowercase__								=    RwkvForCausalLM.from_pretrained(self.model_id    ).to(lowerCamelCase__    )

									lowercase__								=    self.tokenizer("""Hello my name is"""    ,       return_tensors="""pt"""    ).input_ids.to(lowerCamelCase__    )
									lowercase__								=    model.generate(lowerCamelCase__    ,       max_new_tokens=10    )
									lowercase__								=    self.tokenizer.decode(output[0].tolist()    )

									self.assertEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    """Hello my name is Jasmine and I am a newbie to the"""

									lowercase__								=    self.tokenizer("""Hello my name is"""    ,       return_tensors="""pt"""    ).input_ids.to(lowerCamelCase__    )
									lowercase__								=    RwkvForCausalLM.from_pretrained(self.model_id    ,       torch_dtype=torch.bfloataa    ).to(lowerCamelCase__    )

									lowercase__								=    model.generate(lowerCamelCase__    ,       max_new_tokens=10    )
									lowercase__								=    self.tokenizer.decode(output[0].tolist()    )

									self.assertEqual(lowerCamelCase__    ,       lowerCamelCase__    )








'''simple docstring'''



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()


__A       =      logging.getLogger(__name__)





@dataclass
class      A							:
			lowerCamelCase							:							str    						=					field(
			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    default="""NER"""						,  metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}			)
			lowerCamelCase							:							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.
			lowerCamelCase							:							Optional[str]    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,  )





@dataclass
class      A							:
			lowerCamelCase							:							str    						=					field(
			    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}			)
			lowerCamelCase							:							Optional[str]    						=					field(
			    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}			)
			lowerCamelCase							:							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."""
			        )
			    }						,  )
			lowerCamelCase							:							bool    						=					field(
			    default=__UpperCAmelCase						,  metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)





def 	_A						(							):
						# 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.
						lowercase__								=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)		)
						lowercase__	,  lowercase__	,  lowercase__								=    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."""		)

						lowercase__								=    import_module("""tasks"""		)

						try:
												lowercase__								=    getattr(lowercase__						,						model_args.task_type		)
												lowercase__								=    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"""						,						lowercase__		)

						# Prepare Token Classification task
						lowercase__								=    token_classification_task.get_labels(data_args.labels		)
						lowercase__								=    dict(enumerate(lowercase__		)		)
						lowercase__								=    len(lowercase__		)

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

						lowercase__								=    AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,						num_labels=lowercase__						,						idalabel=lowercase__						,						labelaid={label: i for i, label in enumerate(lowercase__		)}						,						cache_dir=model_args.cache_dir						,						)
						lowercase__								=    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():
												lowercase__								=    TFAutoModelForTokenClassification.from_pretrained(
												    model_args.model_name_or_path						,						from_pt=bool(""".bin""" in model_args.model_name_or_path		)						,						config=lowercase__						,						cache_dir=model_args.cache_dir						,						)

						# Get datasets
						lowercase__								=    (
						    TFTokenClassificationDataset(
						        token_classification_task=lowercase__						,						data_dir=data_args.data_dir						,						tokenizer=lowercase__						,						labels=lowercase__						,						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
						)
						lowercase__								=    (
						    TFTokenClassificationDataset(
						        token_classification_task=lowercase__						,						data_dir=data_args.data_dir						,						tokenizer=lowercase__						,						labels=lowercase__						,						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(lowercase__						,						lowercase__		) -> Tuple[List[int], List[int]]:
												lowercase__								=    np.argmax(lowercase__						,						axis=2		)
												lowercase__	,  lowercase__								=    preds.shape
												lowercase__								=    [[] for _ in range(lowercase__		)]
												lowercase__								=    [[] for _ in range(lowercase__		)]

												for i in range(lowercase__		):
																		for j in range(lowercase__		):
																								if label_ids[i, j] != -100:
																														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(lowercase__		) -> Dict:
												lowercase__	,  lowercase__								=    align_predictions(p.predictions						,						p.label_ids		)

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

						# Initialize our Trainer
						lowercase__								=    TFTrainer(
						    model=lowercase__						,						args=lowercase__						,						train_dataset=train_dataset.get_dataset() if train_dataset else None						,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None						,						compute_metrics=lowercase__						,						)

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

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

												lowercase__								=    trainer.evaluate()
												lowercase__								=    os.path.join(training_args.output_dir						,						"""eval_results.txt"""		)

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

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

																		results.update(lowercase__		)

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

												lowercase__	,  lowercase__	,  lowercase__								=    trainer.predict(test_dataset.get_dataset()		)
												lowercase__	,  lowercase__								=    align_predictions(lowercase__						,						lowercase__		)
												lowercase__								=    classification_report(lowercase__						,						lowercase__		)

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

												lowercase__								=    os.path.join(training_args.output_dir						,						"""test_results.txt"""		)

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

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

												with open(lowercase__						,						"""w"""		) as writer:
																		with open(os.path.join(data_args.data_dir						,						"""test.txt"""		)						,						"""r"""		) as f:
																								lowercase__								=    0

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

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

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

						return results


if __name__ == "__main__":
				main()






