





"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						UpperCamelCase_	 ="""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None			,		_lowercase=None  ):
						if attention_mask is None:
												_UpperCamelCase     :   List[str]      =       np.where(input_ids != config.pad_token_id			,		1			,		0  )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}




@require_flax
class 					_a		:






				def __init__(					self						:       int,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple=1_3,					lowerCAmelCase__						:       Tuple=7,					lowerCAmelCase__						:       Dict=True,					lowerCAmelCase__						:       Optional[int]=False,					lowerCAmelCase__						:       int=9_9,					lowerCAmelCase__						:       Tuple=1_6,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       Any=4,					lowerCAmelCase__						:       Union[str, Any]="gelu",					lowerCAmelCase__						:       Optional[Any]=0.1,					lowerCAmelCase__						:       Tuple=0.1,					lowerCAmelCase__						:       Dict=2_0,					lowerCAmelCase__						:       List[Any]=2,					lowerCAmelCase__						:       int=1,					lowerCAmelCase__						:       Dict=0,					lowerCAmelCase__						:       Tuple=1_6,					lowerCAmelCase__						:       Union[str, Any]=1_6,					lowerCAmelCase__						:       int=0.02,					)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       parent
										_UpperCamelCase     :   Optional[Any]      =       batch_size
										_UpperCamelCase     :   Any      =       seq_length
										_UpperCamelCase     :   List[Any]      =       is_training
										_UpperCamelCase     :   Optional[Any]      =       use_labels
										_UpperCamelCase     :   Optional[int]      =       vocab_size
										_UpperCamelCase     :   Optional[int]      =       hidden_size
										_UpperCamelCase     :   List[Any]      =       num_hidden_layers
										_UpperCamelCase     :   Any      =       num_attention_heads
										_UpperCamelCase     :   Union[str, Any]      =       intermediate_size
										_UpperCamelCase     :   Optional[Any]      =       hidden_act
										_UpperCamelCase     :   List[str]      =       hidden_dropout_prob
										_UpperCamelCase     :   Optional[Any]      =       attention_probs_dropout_prob
										_UpperCamelCase     :   List[Any]      =       max_position_embeddings
										_UpperCamelCase     :   int      =       eos_token_id
										_UpperCamelCase     :   Union[str, Any]      =       pad_token_id
										_UpperCamelCase     :   int      =       bos_token_id
										_UpperCamelCase     :   Any      =       embed_dim
										_UpperCamelCase     :   str      =       word_embed_proj_dim
										_UpperCamelCase     :   Any      =       initializer_range
										_UpperCamelCase     :   Optional[Any]      =       False






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




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1],					self.vocab_size				),					3,					self.vocab_size				)
										_UpperCamelCase     :   str      =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),					dtype=np.intaa				)),					-1				)

										_UpperCamelCase     :   List[str]      =       OPTConfig(
										    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					ffn_dim=self.intermediate_size,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					eos_token_id=self.eos_token_id,					bos_token_id=self.bos_token_id,					pad_token_id=self.pad_token_id,					embed_dim=self.embed_dim,					is_encoder_decoder=lowerCAmelCase__,					word_embed_proj_dim=self.word_embed_proj_dim,					initializer_range=self.initializer_range,					use_cache=lowerCAmelCase__,					)
										_UpperCamelCase     :   List[str]      =       prepare_opt_inputs_dict(lowerCAmelCase__,					lowerCAmelCase__				)
										return config, inputs_dict






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




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   int      =       self.prepare_config_and_inputs()
										return config, inputs_dict






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       str				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       2_0
										_UpperCamelCase     :   int      =       model_class_name(lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       inputs_dict['''input_ids''']
										_UpperCamelCase     :   Any      =       inputs_dict['''attention_mask''']

										_UpperCamelCase     :   Dict      =       model.init_cache(input_ids.shape[0],					lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       jnp.ones((input_ids.shape[0], max_length),					dtype='''i4'''				)

										_UpperCamelCase     :   Dict      =       jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1				)[None, :],					(input_ids.shape[0], input_ids.shape[-1] - 1),					)
										_UpperCamelCase     :   Any      =       model(
										    input_ids[:, :-1],					attention_mask=lowerCAmelCase__,					past_key_values=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)

										_UpperCamelCase     :   Optional[Any]      =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],					dtype='''i4'''				)
										_UpperCamelCase     :   str      =       model(
										    input_ids[:, -1:],					attention_mask=lowerCAmelCase__,					past_key_values=outputs_cache.past_key_values,					position_ids=lowerCAmelCase__,					)

										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[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						:       Union[str, Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       2_0
										_UpperCamelCase     :   str      =       model_class_name(lowerCAmelCase__				)

										_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       (
										    inputs_dict['''input_ids'''],
										    inputs_dict['''attention_mask'''],
										)

										_UpperCamelCase     :   str      =       jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])				),
										    ],					axis=-1,					)

										_UpperCamelCase     :   str      =       model.init_cache(input_ids.shape[0],					lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1				)[None, :],					(input_ids.shape[0], input_ids.shape[-1] - 1),					)

										_UpperCamelCase     :   List[Any]      =       model(
										    input_ids[:, :-1],					attention_mask=lowerCAmelCase__,					past_key_values=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)
										_UpperCamelCase     :   str      =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],					dtype='''i4'''				)
										_UpperCamelCase     :   List[Any]      =       model(
										    input_ids[:, -1:],					past_key_values=outputs_cache.past_key_values,					attention_mask=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)

										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__				)

										_UpperCamelCase     :   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_flax
class 					_a		( _lowerCAmelCase      ,							unittest.TestCase      ,							_lowerCAmelCase							):
				UpperCamelCase								=       (FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
				UpperCamelCase								=       () if is_flax_available() else ()






				def 							snake_case      (					self						:       List[str]				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       FlaxOPTModelTester(self				)






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




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       Optional[Any]				)      -> str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																_UpperCamelCase     :   str      =       model_class_name.from_pretrained('''facebook/opt-125m'''				)
																_UpperCamelCase     :   Optional[Any]      =       np.ones((1, 1)				) * model.config.eos_token_id
																_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)




@require_sentencepiece
@require_flax
class 					_a		( unittest.TestCase							):






				@slow
				def 							snake_case      (					self						:       Union[str, Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
										_UpperCamelCase     :   Dict      =       jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										_UpperCamelCase     :   Tuple      =       model(input_ids=lowerCAmelCase__				).last_hidden_state
										_UpperCamelCase     :   Optional[int]      =       (1, 1_1, 5_1_2)
										self.assertEqual(output.shape,					lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       jnp.array(
										    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]				)
										self.assertTrue(jnp.allclose(output[:, :3, :3],					lowerCAmelCase__,					atol=4e-2				)				)




@require_flax
@slow
class 					_a		( unittest.TestCase							):






				def 							snake_case      (					self						:       str				)      -> List[Any]:




										'''simple docstring'''



										super().setUp()
										_UpperCamelCase     :   Any      =       '''facebook/opt-350m'''






				def 							snake_case      (					self						:       List[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       FlaxOPTForCausalLM.from_pretrained(self.path_model				)
										_UpperCamelCase     :   str      =       GPTaTokenizer.from_pretrained(self.path_model				)

										_UpperCamelCase     :   Any      =       [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of''',
										    '''Paris is the capital of France and''',
										    '''Computers and mobile phones have taken''',
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										_UpperCamelCase     :   Optional[int]      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax''',					padding=lowerCAmelCase__,					add_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       model(inputs.input_ids,					attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
										_UpperCamelCase     :   List[str]      =       jnp.array(
										    [
										        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
										        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
										        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
										        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
										    ]				)
										self.assertTrue(jnp.allclose(lowerCAmelCase__,					lowerCAmelCase__,					atol=4e-2				)				)

										_UpperCamelCase     :   Union[str, Any]      =       jax.jit(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       model(inputs.input_ids,					attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
										self.assertTrue(jnp.allclose(lowerCAmelCase__,					lowerCAmelCase__,					atol=4e-2				)				)




@require_flax
@slow
class 					_a		( unittest.TestCase							):






				@property
				def 							snake_case      (					self						:       str				)      -> Any:




										'''simple docstring'''



										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]






				def 							snake_case      (					self						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       '''facebook/opt-125m'''

										_UpperCamelCase     :   Dict      =       [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of New York, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										_UpperCamelCase     :   Any      =       []

										_UpperCamelCase     :   str      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)

										for prompt in self.prompts:
																_UpperCamelCase     :   Dict      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax'''				).input_ids

																_UpperCamelCase     :   Any      =       model.generate(lowerCAmelCase__,					max_length=1_0				)
																_UpperCamelCase     :   Union[str, Any]      =       generated_ids[0]

																_UpperCamelCase     :   Dict      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''facebook/opt-350m'''

										_UpperCamelCase     :   List[str]      =       [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of San Francisco, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										_UpperCamelCase     :   Dict      =       []
										_UpperCamelCase     :   Any      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)

										for prompt in self.prompts:
																_UpperCamelCase     :   List[Any]      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax'''				).input_ids

																_UpperCamelCase     :   List[Any]      =       model.generate(lowerCAmelCase__,					max_length=1_0				)
																_UpperCamelCase     :   List[str]      =       generated_ids[0]

																_UpperCamelCase     :   Optional[Any]      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       '''facebook/opt-125m'''
										_UpperCamelCase     :   Optional[Any]      =       [
										    '''Today is a beautiful day and I want to thank''',
										    '''In the city of Rome Canaver Canaver Canaver Canaver''',
										]
										_UpperCamelCase     :   Any      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer(
										    [
										        '''Today is a beautiful day and I want to''',
										        '''In the city of''',
										    ],					return_tensors='''jax''',					padding=lowerCAmelCase__,					)

										_UpperCamelCase     :   Any      =       jax.jit(model.generate				)

										_UpperCamelCase     :   Optional[int]      =       jit_generate(inputs['''input_ids'''],					attention_mask=inputs['''attention_mask''']				).sequences

										_UpperCamelCase     :   List[Any]      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)

										self.assertIsNotNone(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[int]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       '''facebook/opt-350m'''

										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       '''left'''

										# use different length sentences to test batching
										_UpperCamelCase     :   str      =       [
										    '''Hello, my dog is a little''',
										    '''Today, I''',
										]

										_UpperCamelCase     :   int      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax''',					padding=lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       inputs['''input_ids''']

										_UpperCamelCase     :   Optional[int]      =       model.generate(input_ids=lowerCAmelCase__,					attention_mask=inputs['''attention_mask'''],					trace=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       tokenizer(sentences[0],					return_tensors='''jax'''				).input_ids
										_UpperCamelCase     :   Union[str, Any]      =       model.generate(input_ids=lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
										_UpperCamelCase     :   Any      =       tokenizer(sentences[1],					return_tensors='''jax'''				).input_ids
										_UpperCamelCase     :   Union[str, Any]      =       model.generate(input_ids=lowerCAmelCase__,					max_length=model.config.max_length - num_paddings				)

										_UpperCamelCase     :   Tuple      =       tokenizer.batch_decode(outputs[0],					skip_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       tokenizer.decode(output_non_padded[0][0],					skip_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   Optional[Any]      =       tokenizer.decode(output_padded[0][0],					skip_special_tokens=lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       [
										    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
										    '''Today, I was in the middle of a conversation with a friend about the''',
										]
										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)
										self.assertListEqual(lowerCAmelCase__,					[non_padded_sentence, padded_sentence]				)





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()


SCREAMING_SNAKE_CASE__				=							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=lowerCAmelCase__       ,		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=lowerCAmelCase__       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		bool            =				field(default=lowerCAmelCase__       ,		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=lowerCAmelCase__       ,		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=lowerCAmelCase__       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}							)






def 				__SCREAMING_SNAKE_CASE  (						)	->		int:
				# 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(SCREAMING_SNAKE_CASE       ,				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'       ,				SCREAMING_SNAKE_CASE     )

				# Prepare Token Classification task
				__lowercase								=   token_classification_task.get_labels(data_args.labels     )
				__lowercase								=   dict(enumerate(SCREAMING_SNAKE_CASE     )     )
				__lowercase								=   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.

				__lowercase								=   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       ,				)
				__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=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    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
				)
				__lowercase								=   (
				    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]]:
								__lowercase								=   np.argmax(SCREAMING_SNAKE_CASE       ,				axis=2     )
								__lowercase						,   __lowercase								=   preds.shape
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]
								__lowercase								=   [[] 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] != -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(SCREAMING_SNAKE_CASE			: EvalPrediction     ) -> Dict:
								__lowercase						,   __lowercase								=   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
				__lowercase								=   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
				__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(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:
								__lowercase								=   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       ,				)

								__lowercase						,   __lowercase						,   __lowercase								=   trainer.predict(test_dataset.get_dataset()     )
								__lowercase						,   __lowercase								=   align_predictions(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								__lowercase								=   classification_report(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

								logger.info('\n%s'       ,				SCREAMING_SNAKE_CASE     )

								__lowercase								=   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
								__lowercase								=   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:
																__lowercase								=   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]:
																								__lowercase								=   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()





