






from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)


@require_tf
class     __SCREAMING_SNAKE_CASE					:
						_SCREAMING_SNAKE_CASE	:       str   =					XGLMConfig
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					{}
						_SCREAMING_SNAKE_CASE	:       Tuple   =					'''gelu'''






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase=14     ,						_UpperCamelCase=7     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=99     ,						_UpperCamelCase=32     ,						_UpperCamelCase=2     ,						_UpperCamelCase=4     ,						_UpperCamelCase=37     ,						_UpperCamelCase="gelu"     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=0.1     ,						_UpperCamelCase=5_12     ,						_UpperCamelCase=0.02     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						parent
												lowerCAmelCase__    						=						batch_size
												lowerCAmelCase__    						=						seq_length
												lowerCAmelCase__    						=						is_training
												lowerCAmelCase__    						=						use_input_mask
												lowerCAmelCase__    						=						use_labels
												lowerCAmelCase__    						=						vocab_size
												lowerCAmelCase__    						=						d_model
												lowerCAmelCase__    						=						num_hidden_layers
												lowerCAmelCase__    						=						num_attention_heads
												lowerCAmelCase__    						=						ffn_dim
												lowerCAmelCase__    						=						activation_function
												lowerCAmelCase__    						=						activation_dropout
												lowerCAmelCase__    						=						attention_dropout
												lowerCAmelCase__    						=						max_position_embeddings
												lowerCAmelCase__    						=						initializer_range
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						0
												lowerCAmelCase__    						=						2
												lowerCAmelCase__    						=						1






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return XGLMConfig.from_pretrained('facebook/xglm-564M'   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						tf.clip_by_value(
												    ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size   )     ,						clip_value_min=0     ,						clip_value_max=3   )

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

												lowerCAmelCase__    						=						self.get_config()

												lowerCAmelCase__    						=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,						2   )

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return 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=_UpperCamelCase     ,						bos_token_id=self.bos_token_id     ,						eos_token_id=self.eos_token_id     ,						pad_token_id=self.pad_token_id     ,						return_dict=_UpperCamelCase     ,						)







						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.prepare_config_and_inputs()

												(
												    (
												    lowerCAmelCase__
												)						,       (
												    lowerCAmelCase__
												)						,       (
												    lowerCAmelCase__
												)						,       (
												    lowerCAmelCase__
												)						,       
												)    						=						config_and_inputs

												lowerCAmelCase__    						=						{
												    'input_ids': input_ids,
												    'head_mask': head_mask,
												}

												return config, inputs_dict




@require_tf
class     __SCREAMING_SNAKE_CASE					(   __lowercase		,					__lowercase		,					unittest.TestCase):
						_SCREAMING_SNAKE_CASE	:       int   =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE	:       str   =					(TFXGLMForCausalLM,) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE	:       List[Any]   =					(
						    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
						)
						_SCREAMING_SNAKE_CASE	:       Any   =					False
						_SCREAMING_SNAKE_CASE	:       int   =					False
						_SCREAMING_SNAKE_CASE	:       int   =					False






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						TFXGLMModelTester(self   )
												lowerCAmelCase__    						=						ConfigTester(self     ,						config_class=_UpperCamelCase     ,						n_embd=37   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__    						=						TFXGLMModel.from_pretrained(_UpperCamelCase   )
																		self.assertIsNotNone(_UpperCamelCase   )







						@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'   )
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												super().test_resize_token_embeddings()




@require_tf
class     __SCREAMING_SNAKE_CASE					(   unittest.TestCase):






						@slow
						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=True   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )
												lowerCAmelCase__    						=						tf.convert_to_tensor([[2, 2_68, 98_65]]     ,						dtype=tf.intaa   )  # The dog
												# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
												# fmt: off
												lowerCAmelCase__    						=						[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
												# fmt: on
												lowerCAmelCase__    						=						model.generate(_UpperCamelCase     ,						do_sample=_UpperCamelCase     ,						num_beams=1   )
												if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()     ,						_UpperCamelCase   )






						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'   )
												lowerCAmelCase__    						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )

												tf.random.set_seed(0   )
												lowerCAmelCase__    						=						tokenizer('Today is a nice day and'     ,						return_tensors='tf'   )
												lowerCAmelCase__    						=						tokenized.input_ids
												# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
												with tf.device(':/CPU:0'   ):
																		lowerCAmelCase__    						=						model.generate(_UpperCamelCase     ,						do_sample=_UpperCamelCase     ,						seed=[7, 0]   )
												lowerCAmelCase__    						=						tokenizer.decode(output_ids[0]     ,						skip_special_tokens=_UpperCamelCase   )

												lowerCAmelCase__    						=						(
												    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
												)
												self.assertEqual(_UpperCamelCase     ,						_UpperCamelCase   )







						@slow
						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )
												lowerCAmelCase__    						=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'   )

												lowerCAmelCase__    						=						'left'

												# use different length sentences to test batching
												lowerCAmelCase__    						=						[
												    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
												    'left-padding, such as in batched generation. The output for the sequence below should be the same '
												    'regardless of whether left padding is applied or not. When',
												    'Hello, my dog is a little',
												]

												lowerCAmelCase__    						=						tokenizer(_UpperCamelCase     ,						return_tensors='tf'     ,						padding=_UpperCamelCase   )
												lowerCAmelCase__    						=						inputs['input_ids']

												lowerCAmelCase__    						=						model.generate(input_ids=_UpperCamelCase     ,						attention_mask=inputs['attention_mask']     ,						max_new_tokens=12   )

												lowerCAmelCase__    						=						tokenizer(sentences[0]     ,						return_tensors='tf'   ).input_ids
												lowerCAmelCase__    						=						model.generate(input_ids=_UpperCamelCase     ,						max_new_tokens=12   )

												lowerCAmelCase__    						=						tokenizer(sentences[1]     ,						return_tensors='tf'   ).input_ids
												lowerCAmelCase__    						=						model.generate(input_ids=_UpperCamelCase     ,						max_new_tokens=12   )

												lowerCAmelCase__    						=						tokenizer.batch_decode(_UpperCamelCase     ,						skip_special_tokens=_UpperCamelCase   )
												lowerCAmelCase__    						=						tokenizer.decode(output_non_padded[0]     ,						skip_special_tokens=_UpperCamelCase   )
												lowerCAmelCase__    						=						tokenizer.decode(output_padded[0]     ,						skip_special_tokens=_UpperCamelCase   )

												lowerCAmelCase__    						=						[
												    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
												    'left-padding, such as in batched generation. The output for the sequence below should be the same '
												    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
												    'a single',
												    'Hello, my dog is a little bit of a shy one, but he is very friendly',
												]
												self.assertListEqual(_UpperCamelCase     ,						_UpperCamelCase   )
												self.assertListEqual(_UpperCamelCase     ,						[non_padded_sentence, padded_sentence]   )













import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__snake_case    :    List[Any]         =     logging.getLogger(__name__)


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




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







def 	_UpperCamelCase   (    )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__    						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)
						if len(sys.argv		) == 2 and sys.argv[1].endswith('.json'		):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
						else:
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						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.'		)

						lowerCAmelCase__    						=						import_module('tasks'		)
						try:
												lowerCAmelCase__    						=						getattr(UpperCamelCase_    ,       model_args.task_type		)
												lowerCAmelCase__    						=						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 if training_args.local_rank in [-1, 0] else logging.WARN    ,       )
						logger.warning(
						    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'    ,       training_args.local_rank    ,       training_args.device    ,       training_args.n_gpu    ,       bool(training_args.local_rank != -1		)    ,       training_args.fpaa    ,       )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank		):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('Training/evaluation parameters %s'    ,       UpperCamelCase_		)

						# Set seed
						set_seed(training_args.seed		)

						# Prepare CONLL-2003 task
						lowerCAmelCase__    						=						token_classification_task.get_labels(data_args.labels		)
						lowerCAmelCase__    						=						dict(enumerate(UpperCamelCase_		)		)
						lowerCAmelCase__    						=						len(UpperCamelCase_		)

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

						lowerCAmelCase__    						=						AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,       num_labels=UpperCamelCase_    ,       idalabel=UpperCamelCase_    ,       labelaid={label: i for i, label in enumerate(UpperCamelCase_		)}    ,       cache_dir=model_args.cache_dir    ,       )
						lowerCAmelCase__    						=						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    ,       )
						lowerCAmelCase__    						=						AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path    ,       from_tf=bool('.ckpt' in model_args.model_name_or_path		)    ,       config=UpperCamelCase_    ,       cache_dir=model_args.cache_dir    ,       )

						# Get datasets
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       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
						)
						lowerCAmelCase__    						=						(
						    TokenClassificationDataset(
						        token_classification_task=UpperCamelCase_    ,       data_dir=data_args.data_dir    ,       tokenizer=UpperCamelCase_    ,       labels=UpperCamelCase_    ,       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(UpperCamelCase_       :					np.ndarray    ,       UpperCamelCase_       :					np.ndarray		) -> Tuple[List[int], List[int]]:
												lowerCAmelCase__    						=						np.argmax(UpperCamelCase_    ,       axis=2		)

												lowerCAmelCase__						,       lowerCAmelCase__    						=						preds.shape

												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]
												lowerCAmelCase__    						=						[[] for _ in range(UpperCamelCase_		)]

												for i in range(UpperCamelCase_		):
																		for j in range(UpperCamelCase_		):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														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(UpperCamelCase_       :					EvalPrediction		) -> Dict:
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(p.predictions    ,       p.label_ids		)
												return {
												    "accuracy_score": accuracy_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "precision": precision_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "recall": recall_score(UpperCamelCase_    ,       UpperCamelCase_		),
												    "f1": fa_score(UpperCamelCase_    ,       UpperCamelCase_		),
												}

						# Data collator
						lowerCAmelCase__    						=						DataCollatorWithPadding(UpperCamelCase_    ,       pad_to_multiple_of=8		) if training_args.fpaa else None

						# Initialize our Trainer
						lowerCAmelCase__    						=						Trainer(
						    model=UpperCamelCase_    ,       args=UpperCamelCase_    ,       train_dataset=UpperCamelCase_    ,       eval_dataset=UpperCamelCase_    ,       compute_metrics=UpperCamelCase_    ,       data_collator=UpperCamelCase_    ,       )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path		) else None		)
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir		)

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

												lowerCAmelCase__    						=						trainer.evaluate()

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'eval_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								logger.info('***** Eval results *****'		)
																								for key, value in result.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

																		results.update(UpperCamelCase_		)

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

												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						trainer.predict(UpperCamelCase_		)
												lowerCAmelCase__						,       lowerCAmelCase__    						=						align_predictions(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_results.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								for key, value in metrics.items():
																														logger.info('  %s = %s'    ,       UpperCamelCase_    ,       UpperCamelCase_		)
																														writer.write('%s = %s\n' % (key, value)		)

        # Save predictions
												lowerCAmelCase__    						=						os.path.join(training_args.output_dir    ,       'test_predictions.txt'		)
												if trainer.is_world_process_zero():
																		with open(UpperCamelCase_    ,       'w'		) as writer:
																								with open(os.path.join(data_args.data_dir    ,       'test.txt'		)    ,       'r'		) as f:
																														token_classification_task.write_predictions_to_file(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						return results







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict		)  ->   Union[str, Any]:

						"""simple docstring"""



						main()


if __name__ == "__main__":
					main()





