


"""simple docstring"""


from __future__ import annotations

import unittest

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

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMModel,
		    TFXLMWithLMHeadModel,
		    XLMConfig,
		)
class snake_case					:







		def __init__(    self					:				Tuple			,      a__					:				Any			,      )				->  Tuple:





						'''simple docstring'''







						_A         =	parent
						_A         =	13
						_A         =	7
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	False
						_A         =	False
						_A         =	False
						_A         =	2
						_A         =	99
						_A         =	0
						_A         =	32
						_A         =	2
						_A         =	4
						_A         =	0.1
						_A         =	0.1
						_A         =	5_12
						_A         =	16
						_A         =	2
						_A         =	0.0_2
						_A         =	3
						_A         =	4
						_A         =	"last"
						_A         =	True
						_A         =	None
						_A         =	0







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





						'''simple docstring'''







						_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size						)
						_A         =	random_attention_mask([self.batch_size, self.seq_length]			,      dtype=tf.floataa						)

						_A         =	None
						if self.use_input_lengths:
										_A         =	(
										    ids_tensor([self.batch_size]			,      vocab_size=2						) + self.seq_length - 2
										)  # small variation of seq_length

						_A         =	None
						if self.use_token_type_ids:
										_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.n_langs						)

						_A         =	None
						_A         =	None
						_A         =	None
						if self.use_labels:
										_A         =	ids_tensor([self.batch_size]			,      self.type_sequence_label_size						)
										_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels						)
										_A         =	ids_tensor([self.batch_size]			,      2			,      dtype=tf.floataa						)
										_A         =	ids_tensor([self.batch_size]			,      self.num_choices						)

						_A         =	XLMConfig(
						    vocab_size=self.vocab_size			,      n_special=self.n_special			,      emb_dim=self.hidden_size			,      n_layers=self.num_hidden_layers			,      n_heads=self.num_attention_heads			,      dropout=self.hidden_dropout_prob			,      attention_dropout=self.attention_probs_dropout_prob			,      gelu_activation=self.gelu_activation			,      sinusoidal_embeddings=self.sinusoidal_embeddings			,      asm=self.asm			,      causal=self.causal			,      n_langs=self.n_langs			,      max_position_embeddings=self.max_position_embeddings			,      initializer_range=self.initializer_range			,      summary_type=self.summary_type			,      use_proj=self.use_proj			,      bos_token_id=self.bos_token_id			,      )

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)







		def        a_       (    self					:				Optional[int]			,      a__					:				Dict			,      a__					:				Optional[int]			,      a__					:				int			,      a__					:				Any			,      a__					:				Dict			,      a__					:				Optional[int]			,      a__					:				Optional[Any]			,      a__					:				Optional[int]			,      a__					:				Dict			,      )				->  Any:





						'''simple docstring'''







						_A         =	TFXLMModel(config=a__						)
						_A         =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
						_A         =	model(a__						)

						_A         =	[input_ids, input_mask]
						_A         =	model(a__						)
						self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size)						)







		def        a_       (    self					:				Union[str, Any]			,      a__					:				int			,      a__					:				List[Any]			,      a__					:				Any			,      a__					:				Optional[int]			,      a__					:				int			,      a__					:				Dict			,      a__					:				Dict			,      a__					:				Optional[Any]			,      a__					:				Optional[Any]			,      )				->  Any:





						'''simple docstring'''







						_A         =	TFXLMWithLMHeadModel(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
						_A         =	model(a__						)

						_A         =	outputs

						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)						)







		def        a_       (    self					:				List[Any]			,      a__					:				Any			,      a__					:				Any			,      a__					:				List[Any]			,      a__					:				Dict			,      a__					:				Union[str, Any]			,      a__					:				Tuple			,      a__					:				int			,      a__					:				Dict			,      a__					:				Tuple			,      )				->  str:





						'''simple docstring'''







						_A         =	TFXLMForQuestionAnsweringSimple(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths}

						_A         =	model(a__						)

						self.parent.assertEqual(result.start_logits.shape			,      (self.batch_size, self.seq_length)						)
						self.parent.assertEqual(result.end_logits.shape			,      (self.batch_size, self.seq_length)						)







		def        a_       (    self					:				List[str]			,      a__					:				Any			,      a__					:				str			,      a__					:				Dict			,      a__					:				Tuple			,      a__					:				List[str]			,      a__					:				Tuple			,      a__					:				Dict			,      a__					:				Tuple			,      a__					:				int			,      )				->  Dict:





						'''simple docstring'''







						_A         =	TFXLMForSequenceClassification(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths}

						_A         =	model(a__						)

						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.type_sequence_label_size)						)







		def        a_       (    self					:				Tuple			,      a__					:				Union[str, Any]			,      a__					:				Dict			,      a__					:				Any			,      a__					:				Optional[int]			,      a__					:				Optional[int]			,      a__					:				Optional[Any]			,      a__					:				Any			,      a__					:				List[Any]			,      a__					:				Any			,      )				->  Tuple:





						'''simple docstring'''







						_A         =	self.num_labels
						_A         =	TFXLMForTokenClassification(config=a__						)
						_A         =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						_A         =	model(a__						)
						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)						)







		def        a_       (    self					:				str			,      a__					:				Union[str, Any]			,      a__					:				int			,      a__					:				Optional[int]			,      a__					:				Optional[int]			,      a__					:				str			,      a__					:				List[str]			,      a__					:				Dict			,      a__					:				Union[str, Any]			,      a__					:				Tuple			,      )				->  List[Any]:





						'''simple docstring'''







						_A         =	self.num_choices
						_A         =	TFXLMForMultipleChoice(config=a__						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	{
						    "input_ids": multiple_choice_inputs_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						_A         =	model(a__						)
						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_choices)						)







		def        a_       (    self					:				Optional[Any]						)				->  Dict:





						'''simple docstring'''







						_A         =	self.prepare_config_and_inputs()
						(
						    (
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						
						)         =	config_and_inputs
						_A         =	{
						    "input_ids": input_ids,
						    "token_type_ids": token_type_ids,
						    "langs": token_type_ids,
						    "lengths": input_lengths,
						}
						return config, inputs_dict





@require_tf
class snake_case					(     _UpperCamelCase   , _UpperCamelCase   , unittest.TestCase):
		__UpperCamelCase							=	(
		    (
		        TFXLMModel,
		        TFXLMWithLMHeadModel,
		        TFXLMForSequenceClassification,
		        TFXLMForQuestionAnsweringSimple,
		        TFXLMForTokenClassification,
		        TFXLMForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCamelCase							=	(
		    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Check other models whether language generation is also applicable
		__UpperCamelCase							=	(
		    {
		        'feature-extraction': TFXLMModel,
		        'fill-mask': TFXLMWithLMHeadModel,
		        'question-answering': TFXLMForQuestionAnsweringSimple,
		        'text-classification': TFXLMForSequenceClassification,
		        'text-generation': TFXLMWithLMHeadModel,
		        'token-classification': TFXLMForTokenClassification,
		        'zero-shot': TFXLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCamelCase							=	False
		__UpperCamelCase							=	False







		def        a_       (    self					:				int			,      a__					:				int			,      a__					:				str			,      a__					:				Any			,      a__					:				int			,      a__					:				List[str]						)				->  List[str]:





						'''simple docstring'''







						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("Fast"						)
						):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

						return False







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





						'''simple docstring'''







						_A         =	TFXLMModelTester(self						)
						_A         =	ConfigTester(self			,      config_class=a__			,      emb_dim=37						)







		def        a_       (    self					:				Union[str, Any]						)				->  str:





						'''simple docstring'''







						self.config_tester.run_common_tests()







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*a__						)







		def        a_       (    self					:				List[Any]						)				->  Dict:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*a__						)







		def        a_       (    self					:				Union[str, Any]						)				->  Optional[Any]:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*a__						)







		def        a_       (    self					:				Optional[Any]						)				->  Union[str, Any]:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_token_classification(*a__						)







		def        a_       (    self					:				Union[str, Any]						)				->  List[Any]:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*a__						)







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





						'''simple docstring'''







						for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A         =	TFXLMModel.from_pretrained(a__						)
										self.assertIsNotNone(a__						)





@require_tf
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				List[Any]						)				->  str:





						'''simple docstring'''







						_A         =	TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"						)
						_A         =	tf.convert_to_tensor([[14, 4_47]]			,      dtype=tf.intaa						)  # the president
						_A         =	[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						_A         =	model.generate(a__			,      do_sample=a__						)
						self.assertListEqual(output_ids[0].numpy().tolist()			,      a__						)



"""simple docstring"""


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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a_											=			logging.getLogger(__name__)
@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}   , )





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys())})
		__UpperCamelCase							=	field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase							=	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.'
		        )
		    }   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Overwrite the cached training and evaluation sets'})






def    a__       ( __lowercase      )		->		Dict[str, torch.Tensor]:
				_A         =	default_data_collator(__lowercase      )
				_A         =	batch.pop("pairID"   ,	__lowercase      )
				return batch






def    a__       ( )		->		Optional[Any]:
				# 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.

				_A         =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
				_A     ,						_A     ,						_A         =	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."      )

				# 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"   ,	__lowercase      )

				# Set seed
				set_seed(training_args.seed      )

				try:
								_A         =	hans_tasks_num_labels[data_args.task_name]
				except KeyError:
								raise ValueError("Task not found: %s" % (data_args.task_name)      )

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

				_A         =	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=__lowercase   ,	finetuning_task=data_args.task_name   ,	cache_dir=model_args.cache_dir   ,	)
				_A         =	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   ,	)
				_A         =	AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path   ,	from_tf=bool(".ckpt" in model_args.model_name_or_path      )   ,	config=__lowercase   ,	cache_dir=model_args.cache_dir   ,	)

				# Get datasets
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	)
				    if training_args.do_train
				    else None
				)
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	evaluate=__lowercase   ,	)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				_A         =	Trainer(
				    model=__lowercase   ,	args=__lowercase   ,	train_dataset=__lowercase   ,	eval_dataset=__lowercase   ,	data_collator=__lowercase   ,	)

				# 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_master():
												tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***"      )

								_A         =	trainer.predict(__lowercase      )
								_A         =	output.predictions
								_A         =	np.argmax(__lowercase   ,	axis=1      )

								_A         =	[ex.pairID for ex in eval_dataset]
								_A         =	os.path.join(training_args.output_dir   ,	"hans_predictions.txt"      )
								_A         =	eval_dataset.get_labels()
								if trainer.is_world_master():
												with open(__lowercase   ,	"w"      ) as writer:
																writer.write("pairID,gold_label\n"      )
																for pid, pred in zip(__lowercase   ,	__lowercase      ):
																				writer.write("ex" + str(__lowercase      ) + "," + label_list[int(__lowercase      )] + "\n"      )

								trainer._log(output.metrics      )






def    a__       ( __lowercase      )		->		Dict:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()