

"""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						   =			{
    "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": 128,
    "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, 1.01),
    "suppress_tokens": [0, 1],
    "begin_suppress_tokens": 2,
    "task_specific_params": {"translation": "some_params"},
    "problem_type": "regression",
}





@is_staging_test
class     snake_case	(				unittest.TestCase  ):

				@classmethod
				def 							lowercase_     (	cls							:				List[Any])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			TOKEN
						HfFolder.save_token(__lowerCAmelCase)

				@classmethod
				def 							lowercase_     (	cls							:				str)->   List[str]:



						'''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 							lowercase_     (	self							:				Dict)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			BertConfig(
						    vocab_size=9_9     ,  hidden_size=3_2     ,  num_hidden_layers=5     ,  num_attention_heads=4     ,  intermediate_size=3_7)
						config.push_to_hub("test-config"     ,  use_auth_token=self._token)

						__lowerCAmelCase:       Optional[int]          =			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:       Dict          =			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 							lowercase_     (	self							:				Tuple)->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			BertConfig(
						    vocab_size=9_9     ,  hidden_size=3_2     ,  num_hidden_layers=5     ,  num_attention_heads=4     ,  intermediate_size=3_7)
						config.push_to_hub("valid_org/test-config-org"     ,  use_auth_token=self._token)

						__lowerCAmelCase:       Any          =			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:       Optional[Any]          =			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 							lowercase_     (	self							:				Any)->   List[str]:



						'''simple docstring'''


						CustomConfig.register_for_auto_class()
						__lowerCAmelCase:       str          =			CustomConfig(attribute=4_2)

						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:       Dict          =			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     ,  4_2)





class     snake_case	(				unittest.TestCase  ):

				def 							lowercase_     (	self							:				int)->   int:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			GPTaConfig()

						# attempt to modify each of int/float/bool/str config records and verify they were updated
						__lowerCAmelCase:       Optional[int]          =			c.n_embd + 1  # int
						__lowerCAmelCase:       str          =			c.resid_pdrop + 1.0  # float
						__lowerCAmelCase:       List[Any]          =			not c.scale_attn_weights  # bool
						__lowerCAmelCase:       str          =			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 							lowercase_     (	self							:				Any)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			PretrainedConfig()
						__lowerCAmelCase:       Any          =			[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:       List[Any]          =			[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 							lowercase_     (	self							:				str)->   Dict:



						'''simple docstring'''


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

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

						self.assertIsNotNone(__lowerCAmelCase)

				def 							lowercase_     (	self							:				int)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			mock.Mock()
						__lowerCAmelCase:       Optional[Any]          =			5_0_0
						__lowerCAmelCase:       Optional[Any]          =			{}
						__lowerCAmelCase:       str          =			HTTPError
						__lowerCAmelCase:       str          =			{}

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

				def 							lowercase_     (	self							:				List[str])->   Dict:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:       str          =			AutoConfig.from_pretrained("bert-base-cased")
						__lowerCAmelCase:       Union[str, Any]          =			["config.4.0.0.json"]

						with tempfile.TemporaryDirectory() as tmp_dir:
								configuration.save_pretrained(__lowerCAmelCase)
								__lowerCAmelCase:       List[str]          =			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:       Tuple          =			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:       Dict          =			["config.42.0.0.json"]
								__lowerCAmelCase:       int          =			7_6_8
								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:       str          =			AutoConfig.from_pretrained(__lowerCAmelCase)
								self.assertEqual(new_configuration.hidden_size     ,  7_6_8)




				def 							lowercase_     (	self							:				Any)->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			"hf-internal-testing/test-two-configs"

						import transformers as new_transformers

						__lowerCAmelCase:       Any          =			"v4.0.0"
						__lowerCAmelCase      , __lowerCAmelCase:       List[Any]          =			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:       Tuple          =			"v3.0.0"
						__lowerCAmelCase:       Union[str, Any]          =			old_transformers.models.auto.AutoConfig.from_pretrained(__lowerCAmelCase)
						self.assertEqual(old_configuration.hidden_size     ,  7_6_8)



'''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()




