



"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures/vocab.json""")
SCREAMING_SNAKE_CASE_				= get_tests_dir("""fixtures""")




class 			snake_case_						(						unittest.TestCase				):
							__lowerCAmelCase    					=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     0






							def snake_case_  (   self      ):
									a_     :      str 						=     AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"      )
									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      int 						=     WavaVecaConfig()
											a_     :      List[str] 						=     AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"      )

											# save in new folder
											model_config.save_pretrained(a_      )
											processor.save_pretrained(a_      )

											a_     :      Any 						=     AutoProcessor.from_pretrained(a_      )

									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									with tempfile.TemporaryDirectory() as tmpdirname:
											# copy relevant files
											copyfile(a_			,     os.path.join(a_			,     a_      )      )
											copyfile(a_			,     os.path.join(a_			,     "vocab.json"      )      )

											a_     :      List[Any] 						=     AutoProcessor.from_pretrained(a_      )

									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      str 						=     WavaVecaFeatureExtractor()
											a_     :      List[str] 						=     AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"      )

											a_     :      List[str] 						=     WavaVecaProcessor(a_			,     a_      )

											# save in new folder
											processor.save_pretrained(a_      )

											# drop `processor_class` in tokenizer
											with open(os.path.join(a_			,     a_      )			,     "r"      ) as f:
													a_     :      Tuple 						=     json.load(a_      )
													config_dict.pop("processor_class"      )

											with open(os.path.join(a_			,     a_      )			,     "w"      ) as f:
													f.write(json.dumps(a_      )      )

											a_     :      Dict 						=     AutoProcessor.from_pretrained(a_      )

									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Optional[int] 						=     WavaVecaFeatureExtractor()
											a_     :      Optional[Any] 						=     AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"      )

											a_     :      List[Any] 						=     WavaVecaProcessor(a_			,     a_      )

											# save in new folder
											processor.save_pretrained(a_      )

											# drop `processor_class` in feature extractor
											with open(os.path.join(a_			,     a_      )			,     "r"      ) as f:
													a_     :      Optional[int] 						=     json.load(a_      )
													config_dict.pop("processor_class"      )

											with open(os.path.join(a_			,     a_      )			,     "w"      ) as f:
													f.write(json.dumps(a_      )      )

											a_     :      Any 						=     AutoProcessor.from_pretrained(a_      )

									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									with tempfile.TemporaryDirectory() as tmpdirname:
											a_     :      Tuple 						=     WavaVecaConfig(processor_class="Wav2Vec2Processor"      )
											model_config.save_pretrained(a_      )
											# copy relevant files
											copyfile(a_			,     os.path.join(a_			,     "vocab.json"      )      )
											# create emtpy sample processor
											with open(os.path.join(a_			,     a_      )			,     "w"      ) as f:
													f.write("{}"      )

											a_     :      List[str] 						=     AutoProcessor.from_pretrained(a_      )

									self.assertIsInstance(a_			,     a_      )






							def snake_case_  (   self      ):
									# If remote code is not set, we will time out when asking whether to load the model.
									with self.assertRaises(a_      ):
											a_     :      str 						=     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"      )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(a_      ):
											a_     :      Dict 						=     AutoProcessor.from_pretrained(
											    "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=a_      )

									a_     :      int 						=     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=a_      )
									self.assertTrue(processor.special_attribute_present      )
									self.assertEqual(processor.__class__.__name__			,     "NewProcessor"      )

									a_     :      str 						=     processor.feature_extractor
									self.assertTrue(feature_extractor.special_attribute_present      )
									self.assertEqual(feature_extractor.__class__.__name__			,     "NewFeatureExtractor"      )

									a_     :      int 						=     processor.tokenizer
									self.assertTrue(tokenizer.special_attribute_present      )
									if is_tokenizers_available():
											self.assertEqual(tokenizer.__class__.__name__			,     "NewTokenizerFast"      )

											# Test we can also load the slow version
											a_     :      Any 						=     AutoProcessor.from_pretrained(
											    "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=a_			,     use_fast=a_      )
											a_     :      List[Any] 						=     new_processor.tokenizer
											self.assertTrue(new_tokenizer.special_attribute_present      )
											self.assertEqual(new_tokenizer.__class__.__name__			,     "NewTokenizer"      )
									else:
											self.assertEqual(tokenizer.__class__.__name__			,     "NewTokenizer"      )






							def snake_case_  (   self      ):
									try:
											AutoConfig.register("custom"			,     a_      )
											AutoFeatureExtractor.register(a_			,     a_      )
											AutoTokenizer.register(a_			,     slow_tokenizer_class=a_      )
											AutoProcessor.register(a_			,     a_      )
											# Trying to register something existing in the Transformers library will raise an error
											with self.assertRaises(a_      ):
													AutoProcessor.register(a_			,     a_      )

											# Now that the config is registered, it can be used as any other config with the auto-API
											a_     :      Union[str, Any] 						=     CustomFeatureExtractor.from_pretrained(a_      )

											with tempfile.TemporaryDirectory() as tmp_dir:
													a_     :      str 						=     os.path.join(a_			,     "vocab.txt"      )
													with open(a_			,     "w"			,     encoding="utf-8"      ) as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]      )      )
													a_     :      List[str] 						=     CustomTokenizer(a_      )

											a_     :      Dict 						=     CustomProcessor(a_			,     a_      )

											with tempfile.TemporaryDirectory() as tmp_dir:
													processor.save_pretrained(a_      )
													a_     :      Optional[Any] 						=     AutoProcessor.from_pretrained(a_      )
													self.assertIsInstance(a_			,     a_      )

									finally:
											if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
											if CustomConfig in TOKENIZER_MAPPING._extra_content:
													del TOKENIZER_MAPPING._extra_content[CustomConfig]
											if CustomConfig in PROCESSOR_MAPPING._extra_content:
													del PROCESSOR_MAPPING._extra_content[CustomConfig]






							def snake_case_  (   self      ):




									class 			snake_case_						(						a_				):
																__lowerCAmelCase    					=					False




									class 			snake_case_						(						a_				):
																__lowerCAmelCase    					=					False




									class 			snake_case_						(						a_				):
																__lowerCAmelCase    					=					"AutoFeatureExtractor"
																__lowerCAmelCase    					=					"AutoTokenizer"
																__lowerCAmelCase    					=					False

									try:
											AutoConfig.register("custom"			,     a_      )
											AutoFeatureExtractor.register(a_			,     a_      )
											AutoTokenizer.register(a_			,     slow_tokenizer_class=a_      )
											AutoProcessor.register(a_			,     a_      )
											# If remote code is not set, the default is to use local classes.
											a_     :      List[Any] 						=     AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"      )
											self.assertEqual(processor.__class__.__name__			,     "NewProcessor"      )
											self.assertFalse(processor.special_attribute_present      )
											self.assertFalse(processor.feature_extractor.special_attribute_present      )
											self.assertFalse(processor.tokenizer.special_attribute_present      )

											# If remote code is disabled, we load the local ones.
											a_     :      Optional[int] 						=     AutoProcessor.from_pretrained(
											    "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=a_      )
											self.assertEqual(processor.__class__.__name__			,     "NewProcessor"      )
											self.assertFalse(processor.special_attribute_present      )
											self.assertFalse(processor.feature_extractor.special_attribute_present      )
											self.assertFalse(processor.tokenizer.special_attribute_present      )

											# If remote is enabled, we load from the Hub.
											a_     :      Union[str, Any] 						=     AutoProcessor.from_pretrained(
											    "hf-internal-testing/test_dynamic_processor"			,     trust_remote_code=a_      )
											self.assertEqual(processor.__class__.__name__			,     "NewProcessor"      )
											self.assertTrue(processor.special_attribute_present      )
											self.assertTrue(processor.feature_extractor.special_attribute_present      )
											self.assertTrue(processor.tokenizer.special_attribute_present      )

									finally:
											if "custom" in CONFIG_MAPPING._extra_content:
													del CONFIG_MAPPING._extra_content["custom"]
											if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
													del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
											if CustomConfig in TOKENIZER_MAPPING._extra_content:
													del TOKENIZER_MAPPING._extra_content[CustomConfig]
											if CustomConfig in PROCESSOR_MAPPING._extra_content:
													del PROCESSOR_MAPPING._extra_content[CustomConfig]






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"      )
									self.assertEqual(processor.__class__.__name__			,     "BertTokenizerFast"      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"      )
									self.assertEqual(processor.__class__.__name__			,     "ConvNextImageProcessor"      )




@is_staging_test
class 			snake_case_						(						unittest.TestCase				):
							__lowerCAmelCase    					=					["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]






							@classmethod
							def snake_case_  (   cls      ):
									a_     :      Optional[int] 						=     TOKEN
									HfFolder.save_token(a_      )






							@classmethod
							def snake_case_  (   cls      ):
									try:
											delete_repo(token=cls._token			,     repo_id="test-processor"      )
									except HTTPError:
											pass

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

									try:
											delete_repo(token=cls._token			,     repo_id="test-dynamic-processor"      )
									except HTTPError:
											pass






							def snake_case_  (   self      ):
									a_     :      str 						=     WavaVecaProcessor.from_pretrained(a_      )
									with tempfile.TemporaryDirectory() as tmp_dir:
											processor.save_pretrained(
											    os.path.join(a_			,     "test-processor"      )			,     push_to_hub=a_			,     use_auth_token=self._token      )

											a_     :      Dict 						=     WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""      )
											for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(a_			,     getattr(new_processor.feature_extractor			,     a_      )      )
											self.assertDictEqual(new_processor.tokenizer.get_vocab()			,     processor.tokenizer.get_vocab()      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     WavaVecaProcessor.from_pretrained(a_      )

									with tempfile.TemporaryDirectory() as tmp_dir:
											processor.save_pretrained(
											    os.path.join(a_			,     "test-processor-org"      )			,     push_to_hub=a_			,     use_auth_token=self._token			,     organization="valid_org"			,     )

											a_     :      Optional[Any] 						=     WavaVecaProcessor.from_pretrained("valid_org/test-processor-org"      )
											for k, v in processor.feature_extractor.__dict__.items():
													self.assertEqual(a_			,     getattr(new_processor.feature_extractor			,     a_      )      )
											self.assertDictEqual(new_processor.tokenizer.get_vocab()			,     processor.tokenizer.get_vocab()      )






							def snake_case_  (   self      ):
									CustomFeatureExtractor.register_for_auto_class()
									CustomTokenizer.register_for_auto_class()
									CustomProcessor.register_for_auto_class()

									a_     :      List[Any] 						=     CustomFeatureExtractor.from_pretrained(a_      )

									with tempfile.TemporaryDirectory() as tmp_dir:
											a_     :      str 						=     os.path.join(a_			,     "vocab.txt"      )
											with open(a_			,     "w"			,     encoding="utf-8"      ) as vocab_writer:
													vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens]      )      )
											a_     :      Union[str, Any] 						=     CustomTokenizer(a_      )

									a_     :      int 						=     CustomProcessor(a_			,     a_      )

									with tempfile.TemporaryDirectory() as tmp_dir:
											create_repo(F"""{USER}/test-dynamic-processor"""			,     token=self._token      )
											a_     :      Any 						=     Repository(a_			,     clone_from=F"""{USER}/test-dynamic-processor"""			,     token=self._token      )
											processor.save_pretrained(a_      )

											# This has added the proper auto_map field to the feature extractor config
											self.assertDictEqual(
											    processor.feature_extractor.auto_map			,     {
											        "AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
											        "AutoProcessor": "custom_processing.CustomProcessor",
											    }			,     )

											# This has added the proper auto_map field to the tokenizer config
											with open(os.path.join(a_			,     "tokenizer_config.json"      )      ) as f:
													a_     :      List[Any] 						=     json.load(a_      )
											self.assertDictEqual(
											    tokenizer_config["auto_map"]			,     {
											        "AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
											        "AutoProcessor": "custom_processing.CustomProcessor",
											    }			,     )

											# The code has been copied from fixtures
											self.assertTrue(os.path.isfile(os.path.join(a_			,     "custom_feature_extraction.py"      )      )      )
											self.assertTrue(os.path.isfile(os.path.join(a_			,     "custom_tokenization.py"      )      )      )
											self.assertTrue(os.path.isfile(os.path.join(a_			,     "custom_processing.py"      )      )      )

											repo.push_to_hub()

									a_     :      List[str] 						=     AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""			,     trust_remote_code=a_      )
									# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
									self.assertEqual(new_processor.__class__.__name__			,     "CustomProcessor"      )




"""simple docstring"""
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_				= {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     finetuning_task.lower() if finetuning_task is not None else ""
		if finetuning_task in GLUE_TASKS_NUM_LABELS:
				print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		a_     :      Tuple 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		print(F"""Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)
		print(F"""Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--xlnet_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained XLNet model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
					)
					parser.add_argument(
					    """--finetuning_task""",
					    default=None,
					    type=str,
					    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					print(args)

					convert_xlnet_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
					)