




"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


a_				:    int										=     logging.getLogger(__name__)

class __lowercase(      SCREAMING_SNAKE_CASE_				):




					'''simple docstring'''


					__a						:						str     		= 'sequence-classification'
					def __init__(    self						,	__a     ):
								if type(UpperCamelCase__     ) == dict:
											__lowerCamelCase					:	Any	       =				Namespace(**UpperCamelCase__     )
								__lowerCamelCase					:	int	       =				glue_output_modes[hparams.task]
								__lowerCamelCase					:	int	       =				glue_tasks_num_labels[hparams.task]

								super().__init__(UpperCamelCase__						,	UpperCamelCase__						,	self.mode     )
					def     snake_case_				(    self						,	**__a     ):
								return self.model(**UpperCamelCase__     )
					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Dict	       =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
											__lowerCamelCase					:	Optional[int]	       =				batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

								__lowerCamelCase					:	Optional[int]	       =				self(**UpperCamelCase__     )
								__lowerCamelCase					:	str	       =				outputs[0]

								__lowerCamelCase					:	List[Any]	       =				self.trainer.lr_schedulers[0]['''scheduler''']
								__lowerCamelCase					:	Optional[Any]	       =				{'''loss''': loss, '''rate''': lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss, "log": tensorboard_logs}
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				self.hparams
								__lowerCamelCase					:	Dict	       =				processors[args.task]()
								__lowerCamelCase					:	Any	       =				processor.get_labels()

								for mode in ["train", "dev"]:
											__lowerCamelCase					:	Union[str, Any]	       =				self._feature_file(UpperCamelCase__     )
											if os.path.exists(UpperCamelCase__     ) and not args.overwrite_cache:
														logger.info('Loading features from cached file %s'						,	UpperCamelCase__     )
											else:
														logger.info('Creating features from dataset file at %s'						,	args.data_dir     )
														__lowerCamelCase					:	str	       =				(
														    processor.get_dev_examples(args.data_dir     )
														    if mode == '''dev'''
														    else processor.get_train_examples(args.data_dir     )
														)
														__lowerCamelCase					:	List[str]	       =				convert_examples_to_features(
														    UpperCamelCase__						,	self.tokenizer						,	max_length=args.max_seq_length						,	label_list=self.labels						,	output_mode=args.glue_output_mode						,	)
														logger.info('Saving features into cached file %s'						,	UpperCamelCase__     )
														torch.save(UpperCamelCase__						,	UpperCamelCase__     )
					def     snake_case_				(    self						,	__a						,	__a						,	__a = False     ):
								__lowerCamelCase					:	Tuple	       =				'''dev''' if mode == '''test''' else mode

								__lowerCamelCase					:	Tuple	       =				self._feature_file(UpperCamelCase__     )
								logger.info('Loading features from cached file %s'						,	UpperCamelCase__     )
								__lowerCamelCase					:	List[str]	       =				torch.load(UpperCamelCase__     )
								__lowerCamelCase					:	Dict	       =				torch.tensor([f.input_ids for f in features]						,	dtype=torch.long     )
								__lowerCamelCase					:	Optional[int]	       =				torch.tensor([f.attention_mask for f in features]						,	dtype=torch.long     )
								__lowerCamelCase					:	List[Any]	       =				torch.tensor([f.token_type_ids for f in features]						,	dtype=torch.long     )
								if self.hparams.glue_output_mode == "classification":
											__lowerCamelCase					:	int	       =				torch.tensor([f.label for f in features]						,	dtype=torch.long     )
								elif self.hparams.glue_output_mode == "regression":
											__lowerCamelCase					:	Tuple	       =				torch.tensor([f.label for f in features]						,	dtype=torch.float     )

								return DataLoader(
								    TensorDataset(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__     )						,	batch_size=UpperCamelCase__						,	shuffle=UpperCamelCase__						,	)
					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	Optional[int]	       =				{'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
											__lowerCamelCase					:	int	       =				batch[2] if self.config.model_type in ['''bert''', '''xlnet''', '''albert'''] else None

								__lowerCamelCase					:	Union[str, Any]	       =				self(**UpperCamelCase__     )
								__lowerCamelCase					:	int	       =				outputs[:2]
								__lowerCamelCase					:	Optional[Any]	       =				logits.detach().cpu().numpy()
								__lowerCamelCase					:	Optional[int]	       =				inputs['''labels'''].detach().cpu().numpy()

								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				torch.stack([x['val_loss'] for x in outputs]     ).mean().detach().cpu().item()
								__lowerCamelCase					:	Union[str, Any]	       =				np.concatenate([x['pred'] for x in outputs]						,	axis=0     )

								if self.hparams.glue_output_mode == "classification":
											__lowerCamelCase					:	Optional[Any]	       =				np.argmax(UpperCamelCase__						,	axis=1     )
								elif self.hparams.glue_output_mode == "regression":
											__lowerCamelCase					:	List[Any]	       =				np.squeeze(UpperCamelCase__     )

								__lowerCamelCase					:	int	       =				np.concatenate([x['target'] for x in outputs]						,	axis=0     )
								__lowerCamelCase					:	Optional[int]	       =				[[] for _ in range(out_label_ids.shape[0]     )]
								__lowerCamelCase					:	Optional[int]	       =				[[] for _ in range(out_label_ids.shape[0]     )]

								__lowerCamelCase					:	Optional[Any]	       =				{**{'''val_loss''': val_loss_mean}, **compute_metrics(self.hparams.task						,	UpperCamelCase__						,	UpperCamelCase__     )}

								__lowerCamelCase					:	List[Any]	       =				dict(results.items()     )
								__lowerCamelCase					:	Optional[Any]	       =				results
								return ret, preds_list, out_label_list
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Tuple	       =				self._eval_end(UpperCamelCase__     )
								__lowerCamelCase					:	Optional[int]	       =				ret['''log''']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self._eval_end(UpperCamelCase__     )
								__lowerCamelCase					:	Dict	       =				ret['''log''']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}





					@staticmethod
					def     snake_case_				(    __a						,	__a     ):
								BaseTransformer.add_model_specific_args(UpperCamelCase__						,	UpperCamelCase__     )
								parser.add_argument(
								    '--max_seq_length'						,	default=128						,	type=UpperCamelCase__						,	help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )						,	)

								parser.add_argument(
								    '--task'						,	default=''						,	type=UpperCamelCase__						,	required=UpperCamelCase__						,	help='The GLUE task to run'						,	)
								parser.add_argument(
								    '--gpus'						,	default=0						,	type=UpperCamelCase__						,	help='The number of GPUs allocated for this, it is by default 0 meaning none'						,	)

								parser.add_argument(
								    '--overwrite_cache'						,	action='store_true'						,	help='Overwrite the cached training and evaluation sets'     )

								return parser





def 						UpperCAmelCase				(			)  ->     Dict:
			__lowerCamelCase					:	List[Any]	       =				argparse.ArgumentParser()
			add_generic_args(__UpperCamelCase							,				os.getcwd()     )
			__lowerCamelCase					:	Any	       =				GLUETransformer.add_model_specific_args(__UpperCamelCase							,				os.getcwd()     )
			__lowerCamelCase					:	Tuple	       =				parser.parse_args()

			# If output_dir not provided, a folder will be generated in pwd
			if args.output_dir is None:
						__lowerCamelCase					:	List[str]	       =				os.path.join(
						    './results'							,				f'''{args.task}_{time.strftime("%Y%m%d_%H%M%S"     )}'''							,				)
						os.makedirs(args.output_dir     )

			__lowerCamelCase					:	List[Any]	       =				GLUETransformer(__UpperCamelCase     )
			__lowerCamelCase					:	List[str]	       =				generic_train(__UpperCamelCase							,				__UpperCamelCase     )

			# Optionally, predict on dev set and write to output_dir
			if args.do_predict:
						__lowerCamelCase					:	Any	       =				sorted(glob.glob(os.path.join(args.output_dir							,				'checkpoint-epoch=*.ckpt'     )							,				recursive=__UpperCamelCase     )     )
						__lowerCamelCase					:	int	       =				model.load_from_checkpoint(checkpoints[-1]     )
						return trainer.test(__UpperCamelCase     )


if __name__ == "__main__":
						main()









"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402


a_				:    Optional[int]										=     {
    '''return_dict''': False,
    '''output_hidden_states''': True,
    '''output_attentions''': True,
    '''torchscript''': True,
    '''torch_dtype''': '''float16''',
    '''use_bfloat16''': True,
    '''tf_legacy_loss''': True,
    '''pruned_heads''': {'''a''': 1},
    '''tie_word_embeddings''': False,
    '''is_decoder''': True,
    '''cross_attention_hidden_size''': 1_28,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 50,
    '''min_length''': 3,
    '''do_sample''': True,
    '''early_stopping''': True,
    '''num_beams''': 3,
    '''num_beam_groups''': 3,
    '''diversity_penalty''': 0.5,
    '''temperature''': 2.0,
    '''top_k''': 10,
    '''top_p''': 0.7,
    '''typical_p''': 0.2,
    '''repetition_penalty''': 0.8,
    '''length_penalty''': 0.8,
    '''no_repeat_ngram_size''': 5,
    '''encoder_no_repeat_ngram_size''': 5,
    '''bad_words_ids''': [1, 2, 3],
    '''num_return_sequences''': 3,
    '''chunk_size_feed_forward''': 5,
    '''output_scores''': True,
    '''return_dict_in_generate''': True,
    '''forced_bos_token_id''': 2,
    '''forced_eos_token_id''': 3,
    '''remove_invalid_values''': True,
    '''architectures''': ['''BertModel'''],
    '''finetuning_task''': '''translation''',
    '''id2label''': {0: '''label'''},
    '''label2id''': {'''label''': '''0'''},
    '''tokenizer_class''': '''BertTokenizerFast''',
    '''prefix''': '''prefix''',
    '''bos_token_id''': 6,
    '''pad_token_id''': 7,
    '''eos_token_id''': 8,
    '''sep_token_id''': 9,
    '''decoder_start_token_id''': 10,
    '''exponential_decay_length_penalty''': (5, 1.0_1),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}

@is_staging_test
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					@classmethod
					def     snake_case_				(    cls     ):
								__lowerCamelCase					:	Tuple	       =				TOKEN
								HfFolder.save_token(__a     )
					@classmethod
					def     snake_case_				(    cls     ):
								try:
											delete_repo(token=cls._token						,	repo_id='test-config'     )
								except HTTPError:
											pass

								try:
											delete_repo(token=cls._token						,	repo_id='valid_org/test-config-org'     )
								except HTTPError:
											pass

								try:
											delete_repo(token=cls._token						,	repo_id='test-dynamic-config'     )
								except HTTPError:
											pass
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				BertConfig(
								    vocab_size=99						,	hidden_size=32						,	num_hidden_layers=5						,	num_attention_heads=4						,	intermediate_size=37     )
								config.push_to_hub('test-config'						,	use_auth_token=self._token     )

								__lowerCamelCase					:	Any	       =				BertConfig.from_pretrained(f'''{USER}/test-config'''     )
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(__a						,	getattr(__a						,	__a     )     )

        # Reset repo
								delete_repo(token=self._token						,	repo_id='test-config'     )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(__a						,	repo_id='test-config'						,	push_to_hub=__a						,	use_auth_token=self._token     )

								__lowerCamelCase					:	Any	       =				BertConfig.from_pretrained(f'''{USER}/test-config'''     )
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(__a						,	getattr(__a						,	__a     )     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				BertConfig(
								    vocab_size=99						,	hidden_size=32						,	num_hidden_layers=5						,	num_attention_heads=4						,	intermediate_size=37     )
								config.push_to_hub('valid_org/test-config-org'						,	use_auth_token=self._token     )

								__lowerCamelCase					:	Tuple	       =				BertConfig.from_pretrained('valid_org/test-config-org'     )
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(__a						,	getattr(__a						,	__a     )     )

        # Reset repo
								delete_repo(token=self._token						,	repo_id='valid_org/test-config-org'     )

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    __a						,	repo_id='valid_org/test-config-org'						,	push_to_hub=__a						,	use_auth_token=self._token     )

								__lowerCamelCase					:	Tuple	       =				BertConfig.from_pretrained('valid_org/test-config-org'     )
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(__a						,	getattr(__a						,	__a     )     )





					def     snake_case_				(    self     ):
								CustomConfig.register_for_auto_class()
								__lowerCamelCase					:	Optional[Any]	       =				CustomConfig(attribute=42     )

								config.push_to_hub('test-dynamic-config'						,	use_auth_token=self._token     )

								# This has added the proper auto_map field to the config
								self.assertDictEqual(config.auto_map						,	{'AutoConfig': 'custom_configuration.CustomConfig'}     )

								__lowerCamelCase					:	Tuple	       =				AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config'''						,	trust_remote_code=__a     )
								# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
								self.assertEqual(new_config.__class__.__name__						,	'CustomConfig'     )
								self.assertEqual(new_config.attribute						,	42     )



class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				GPTaConfig()

								# attempt to modify each of int/float/bool/str config records and verify they were updated
								__lowerCamelCase					:	List[str]	       =				c.n_embd + 1  # int
								__lowerCamelCase					:	Dict	       =				c.resid_pdrop + 1.0  # float
								__lowerCamelCase					:	int	       =				not c.scale_attn_weights  # bool
								__lowerCamelCase					:	Optional[int]	       =				c.summary_type + 'foo'  # str
								c.update_from_string(
								    f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''     )
								self.assertEqual(__a						,	c.n_embd						,	'mismatch for key: n_embd'     )
								self.assertEqual(__a						,	c.resid_pdrop						,	'mismatch for key: resid_pdrop'     )
								self.assertEqual(__a						,	c.scale_attn_weights						,	'mismatch for key: scale_attn_weights'     )
								self.assertEqual(__a						,	c.summary_type						,	'mismatch for key: summary_type'     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				PretrainedConfig()
								__lowerCamelCase					:	Dict	       =				[key for key in base_config.__dict__ if key not in config_common_kwargs]
								# If this part of the test fails, you have arguments to addin config_common_kwargs above.
								self.assertListEqual(
								    __a						,	['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version']     )
								__lowerCamelCase					:	int	       =				[key for key, value in config_common_kwargs.items() if value == getattr(__a						,	__a     )]
								if len(__a     ) > 0:
											raise ValueError(
											    'The following keys are set with the default values in'
											    ' `test_configuration_common.config_common_kwargs` pick another value for them:'
											    f''' {", ".join(__a     )}.'''     )
					def     snake_case_				(    self     ):
								with self.assertRaises(__a     ):
											# config is in subfolder, the following should not work without specifying the subfolder
											__lowerCamelCase					:	int	       =				BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'     )

								__lowerCamelCase					:	Optional[int]	       =				BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder'						,	subfolder='bert'     )

								self.assertIsNotNone(__a     )
					def     snake_case_				(    self     ):
								# A mock response for an HTTP head request to emulate server down
								__lowerCamelCase					:	List[str]	       =				mock.Mock()
								__lowerCamelCase					:	Tuple	       =				500
								__lowerCamelCase					:	Tuple	       =				{}
								__lowerCamelCase					:	Optional[Any]	       =				HTTPError
								__lowerCamelCase					:	str	       =				{}

								# Download this model to make sure it's in the cache.
								__lowerCamelCase					:	Optional[int]	       =				BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'     )

								# Under the mock environment we get a 500 error when trying to reach the model.
								with mock.patch('requests.Session.request'						,	return_value=__a     ) as mock_head:
											__lowerCamelCase					:	Union[str, Any]	       =				BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert'     )
											# This check we did call the fake head request
											mock_head.assert_called()
					def     snake_case_				(    self     ):
								# This test is for deprecated behavior and can be removed in v5
								__lowerCamelCase					:	Optional[Any]	       =				BertConfig.from_pretrained(
								    'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json'     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				AutoConfig.from_pretrained('bert-base-cased'     )
								__lowerCamelCase					:	str	       =				['config.4.0.0.json']

								with tempfile.TemporaryDirectory() as tmp_dir:
											configuration.save_pretrained(__a     )
											__lowerCamelCase					:	Optional[int]	       =				2
											json.dump(configuration.to_dict()						,	open(os.path.join(__a						,	'config.4.0.0.json'     )						,	'w'     )     )

											# This should pick the new configuration file as the version of Transformers is > 4.0.0
											__lowerCamelCase					:	Any	       =				AutoConfig.from_pretrained(__a     )
											self.assertEqual(new_configuration.hidden_size						,	2     )

											# Will need to be adjusted if we reach v42 and this test is still here.
											# Should pick the old configuration file as the version of Transformers is < 4.42.0
											__lowerCamelCase					:	Any	       =				['config.42.0.0.json']
											__lowerCamelCase					:	Tuple	       =				768
											configuration.save_pretrained(__a     )
											shutil.move(os.path.join(__a						,	'config.4.0.0.json'     )						,	os.path.join(__a						,	'config.42.0.0.json'     )     )
											__lowerCamelCase					:	Dict	       =				AutoConfig.from_pretrained(__a     )
											self.assertEqual(new_configuration.hidden_size						,	768     )





					def     snake_case_				(    self     ):
								# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
								__lowerCamelCase					:	List[str]	       =				'hf-internal-testing/test-two-configs'

								import transformers as new_transformers

								__lowerCamelCase					:	Tuple	       =				'v4.0.0'
								__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				new_transformers.models.auto.AutoConfig.from_pretrained(
								    __a						,	return_unused_kwargs=__a     )
								self.assertEqual(new_configuration.hidden_size						,	2     )
								# This checks `_configuration_file` ia not kept in the kwargs by mistake.
								self.assertDictEqual(__a						,	{}     )

								# Testing an older version by monkey-patching the version in the module it's used.
								import transformers as old_transformers

								__lowerCamelCase					:	Union[str, Any]	       =				'v3.0.0'
								__lowerCamelCase					:	Optional[int]	       =				old_transformers.models.auto.AutoConfig.from_pretrained(__a     )
								self.assertEqual(old_configuration.hidden_size						,	768     )



