'''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


__lowercase          =  {
    '''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_2_8,
    '''add_cross_attention''': True,
    '''tie_encoder_decoder''': True,
    '''max_length''': 5_0,
    '''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''': 1_0,
    '''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''': 1_0,
    '''exponential_decay_length_penalty''': (5, 1.01),
    '''suppress_tokens''': [0, 1],
    '''begin_suppress_tokens''': 2,
    '''task_specific_params''': {'''translation''': '''some_params'''},
    '''problem_type''': '''regression''',
}






@is_staging_test
class 				a__(					unittest.TestCase	):


			'''simple docstring'''






			@classmethod
			def 							a_       (   cls):




					"""simple docstring"""


					lowerCAmelCase   					=  TOKEN
					HfFolder.save_token(__lowerCAmelCase)



			@classmethod
			def 							a_       (   cls):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


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

        # 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(__lowerCAmelCase       ,    repo_id="""test-config"""       ,    push_to_hub=__lowerCAmelCase       ,    use_auth_token=self._token)

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



			def 							a_       (   self):




					"""simple docstring"""


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

        # 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(
							    __lowerCAmelCase       ,    repo_id="""valid_org/test-config-org"""       ,    push_to_hub=__lowerCAmelCase       ,    use_auth_token=self._token)

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







			def 							a_       (   self):




					"""simple docstring"""


					CustomConfig.register_for_auto_class()
					lowerCAmelCase   					=  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   					=  AutoConfig.from_pretrained(f"{USER}/test-dynamic-config"       ,    trust_remote_code=__lowerCAmelCase)
					# 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 				a__(					unittest.TestCase	):


			'''simple docstring'''






			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  GPTaConfig()

					# attempt to modify each of int/float/bool/str config records and verify they were updated
					lowerCAmelCase   					=  c.n_embd + 1  # int
					lowerCAmelCase   					=  c.resid_pdrop + 1.0  # float
					lowerCAmelCase   					=  not c.scale_attn_weights  # bool
					lowerCAmelCase   					=  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(__lowerCAmelCase       ,    c.n_embd       ,    """mismatch for key: n_embd""")
					self.assertEqual(__lowerCAmelCase       ,    c.resid_pdrop       ,    """mismatch for key: resid_pdrop""")
					self.assertEqual(__lowerCAmelCase       ,    c.scale_attn_weights       ,    """mismatch for key: scale_attn_weights""")
					self.assertEqual(__lowerCAmelCase       ,    c.summary_type       ,    """mismatch for key: summary_type""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  PretrainedConfig()
					lowerCAmelCase   					=  [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(
					    __lowerCAmelCase       ,    ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""])
					lowerCAmelCase   					=  [key for key, value in config_common_kwargs.items() if value == getattr(__lowerCAmelCase       ,    __lowerCAmelCase)]
					if len(__lowerCAmelCase) > 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(__lowerCAmelCase)}.")



			def 							a_       (   self):




					"""simple docstring"""


					with self.assertRaises(__lowerCAmelCase):
							# config is in subfolder, the following should not work without specifying the subfolder
							lowerCAmelCase   					=  BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder""")

					lowerCAmelCase   					=  BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""       ,    subfolder="""bert""")

					self.assertIsNotNone(__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  mock.Mock()
					lowerCAmelCase   					=  500
					lowerCAmelCase   					=  {}
					lowerCAmelCase   					=  HTTPError
					lowerCAmelCase   					=  {}

					# Download this model to make sure it's in the cache.
					lowerCAmelCase   					=  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=__lowerCAmelCase) as mock_head:
							lowerCAmelCase   					=  BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert""")
							# This check we did call the fake head request
							mock_head.assert_called()



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  BertConfig.from_pretrained(
					    """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json""")



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  AutoConfig.from_pretrained("""bert-base-cased""")
					lowerCAmelCase   					=  ["""config.4.0.0.json"""]

					with tempfile.TemporaryDirectory() as tmp_dir:
							configuration.save_pretrained(__lowerCAmelCase)
							lowerCAmelCase   					=  2
							json.dump(configuration.to_dict()       ,    open(os.path.join(__lowerCAmelCase       ,    """config.4.0.0.json""")       ,    """w"""))

							# This should pick the new configuration file as the version of Transformers is > 4.0.0
							lowerCAmelCase   					=  AutoConfig.from_pretrained(__lowerCAmelCase)
							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   					=  ["""config.42.0.0.json"""]
							lowerCAmelCase   					=  768
							configuration.save_pretrained(__lowerCAmelCase)
							shutil.move(os.path.join(__lowerCAmelCase       ,    """config.4.0.0.json""")       ,    os.path.join(__lowerCAmelCase       ,    """config.42.0.0.json"""))
							lowerCAmelCase   					=  AutoConfig.from_pretrained(__lowerCAmelCase)
							self.assertEqual(new_configuration.hidden_size       ,    768)







			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """hf-internal-testing/test-two-configs"""

					import transformers as new_transformers

					lowerCAmelCase   					=  """v4.0.0"""
					lowerCAmelCase					,     lowerCAmelCase   					=  new_transformers.models.auto.AutoConfig.from_pretrained(
					    __lowerCAmelCase       ,    return_unused_kwargs=__lowerCAmelCase)
					self.assertEqual(new_configuration.hidden_size       ,    2)
					# This checks `_configuration_file` ia not kept in the kwargs by mistake.
					self.assertDictEqual(__lowerCAmelCase       ,    {})

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

					lowerCAmelCase   					=  """v3.0.0"""
					lowerCAmelCase   					=  old_transformers.models.auto.AutoConfig.from_pretrained(__lowerCAmelCase)
					self.assertEqual(old_configuration.hidden_size       ,    768)





'''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


__lowercase          =  logging.getLogger(__name__)






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     Any      =       '''sequence-classification'''



			def __init__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					if type(__lowerCAmelCase) == dict:
							lowerCAmelCase   					=  Namespace(**__lowerCAmelCase)
					lowerCAmelCase   					=  glue_output_modes[hparams.task]
					lowerCAmelCase   					=  glue_tasks_num_labels[hparams.task]

					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase       ,    self.mode)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.model(**__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase   					=  outputs[0]

					lowerCAmelCase   					=  self.trainer.lr_schedulers[0]["""scheduler"""]
					lowerCAmelCase   					=  {"""loss""": loss, """rate""": lr_scheduler.get_last_lr()[-1]}
					return {"loss": loss, "log": tensorboard_logs}



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  self.hparams
					lowerCAmelCase   					=  processors[args.task]()
					lowerCAmelCase   					=  processor.get_labels()

					for mode in ["train", "dev"]:
							lowerCAmelCase   					=  self._feature_file(__lowerCAmelCase)
							if os.path.exists(__lowerCAmelCase) and not args.overwrite_cache:
									logger.info("""Loading features from cached file %s"""       ,    __lowerCAmelCase)
							else:
									logger.info("""Creating features from dataset file at %s"""       ,    args.data_dir)
									lowerCAmelCase   					=  (
									    processor.get_dev_examples(args.data_dir)
									    if mode == """dev"""
									    else processor.get_train_examples(args.data_dir)
									)
									lowerCAmelCase   					=  convert_examples_to_features(
									    __lowerCAmelCase       ,    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"""       ,    __lowerCAmelCase)
									torch.save(__lowerCAmelCase       ,    __lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = False):




					"""simple docstring"""


					lowerCAmelCase   					=  """dev""" if mode == """test""" else mode

					lowerCAmelCase   					=  self._feature_file(__lowerCAmelCase)
					logger.info("""Loading features from cached file %s"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  torch.load(__lowerCAmelCase)
					lowerCAmelCase   					=  torch.tensor([f.input_ids for f in features]       ,    dtype=torch.long)
					lowerCAmelCase   					=  torch.tensor([f.attention_mask for f in features]       ,    dtype=torch.long)
					lowerCAmelCase   					=  torch.tensor([f.token_type_ids for f in features]       ,    dtype=torch.long)
					if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase   					=  torch.tensor([f.label for f in features]       ,    dtype=torch.long)
					elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase   					=  torch.tensor([f.label for f in features]       ,    dtype=torch.float)

					return DataLoader(
					    TensorDataset(__lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase)       ,    batch_size=__lowerCAmelCase       ,    shuffle=__lowerCAmelCase       ,    )



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}

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

					lowerCAmelCase   					=  self(**__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase   					=  outputs[:2]
					lowerCAmelCase   					=  logits.detach().cpu().numpy()
					lowerCAmelCase   					=  inputs["""labels"""].detach().cpu().numpy()

					return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  torch.stack([x["""val_loss"""] for x in outputs]).mean().detach().cpu().item()
					lowerCAmelCase   					=  np.concatenate([x["""pred"""] for x in outputs]       ,    axis=0)

					if self.hparams.glue_output_mode == "classification":
							lowerCAmelCase   					=  np.argmax(__lowerCAmelCase       ,    axis=1)
					elif self.hparams.glue_output_mode == "regression":
							lowerCAmelCase   					=  np.squeeze(__lowerCAmelCase)

					lowerCAmelCase   					=  np.concatenate([x["""target"""] for x in outputs]       ,    axis=0)
					lowerCAmelCase   					=  [[] for _ in range(out_label_ids.shape[0])]
					lowerCAmelCase   					=  [[] for _ in range(out_label_ids.shape[0])]

					lowerCAmelCase   					=  {**{"""val_loss""": val_loss_mean}, **compute_metrics(self.hparams.task       ,    __lowerCAmelCase       ,    __lowerCAmelCase)}

					lowerCAmelCase   					=  dict(results.items())
					lowerCAmelCase   					=  results
					return ret, preds_list, out_label_list



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  ret["""log"""]
					return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  self._eval_end(__lowerCAmelCase)
					lowerCAmelCase   					=  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 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					BaseTransformer.add_model_specific_args(__lowerCAmelCase       ,    __lowerCAmelCase)
					parser.add_argument(
					    """--max_seq_length"""       ,    default=128       ,    type=__lowerCAmelCase       ,    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=__lowerCAmelCase       ,    required=__lowerCAmelCase       ,    help="""The GLUE task to run"""       ,    )
					parser.add_argument(
					    """--gpus"""       ,    default=0       ,    type=__lowerCAmelCase       ,    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 				snake_case__							(			)    ->	Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  argparse.ArgumentParser()
		add_generic_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  GLUETransformer.add_model_specific_args(_A      ,   os.getcwd() )
		lowerCAmelCase   					=  parser.parse_args()

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

		lowerCAmelCase   					=  GLUETransformer(_A )
		lowerCAmelCase   					=  generic_train(_A      ,   _A )

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


if __name__ == "__main__":
						main()




