





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


__lowercase       :List[str]       =  get_tests_dir("fixtures/dummy_feature_extractor_config.json")
__lowercase       :str       =  get_tests_dir("fixtures/vocab.json")
__lowercase       :Optional[int]       =  get_tests_dir("fixtures")



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

							def      A_  (      self				:     Optional[Any]    )	->int:
													SCREAMING_SNAKE_CASE__     :				Dict   =					0

							def      A_  (      self				:     Any    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )
													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Union[str, Any]    )	->List[str]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					WavaVecaConfig()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )

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

																			SCREAMING_SNAKE_CASE__     :				str   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     int    )	->List[str]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			# copy relevant files
																			copyfile(a							,	os.path.join(a							,	a    )    )
																			copyfile(a							,	os.path.join(a							,	"vocab.json"    )    )

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     List[Any]    )	->Tuple:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					WavaVecaFeatureExtractor()
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"    )

																			SCREAMING_SNAKE_CASE__     :				Any   =					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:
																									SCREAMING_SNAKE_CASE__     :				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    )    )

																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     List[str]    )	->Optional[Any]:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					WavaVecaFeatureExtractor()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h"    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					json.load(a    )
																									config_dict.pop("processor_class"    )

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

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Union[str, Any]    )	->str:
													with tempfile.TemporaryDirectory() as tmpdirname:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					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("{}"    )

																			SCREAMING_SNAKE_CASE__     :				Tuple   =					AutoProcessor.from_pretrained(a    )

													self.assertIsInstance(a							,	a    )

							def      A_  (      self				:     Optional[Any]    )	->Optional[int]:
													# If remote code is not set, we will time out when asking whether to load the model.
													with self.assertRaises(a    ):
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor"    )
													# If remote code is disabled, we can't load this config.
													with self.assertRaises(a    ):
																			SCREAMING_SNAKE_CASE__     :				Any   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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"    )

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

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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
																			SCREAMING_SNAKE_CASE__     :				int   =					AutoProcessor.from_pretrained(
																			    "hf-internal-testing/test_dynamic_processor"							,	trust_remote_code=a							,	use_fast=a    )
																			SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     Tuple    )	->List[Any]:
													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
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					CustomFeatureExtractor.from_pretrained(a    )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																									SCREAMING_SNAKE_CASE__     :				int   =					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]    )    )
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					CustomTokenizer(a    )

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					CustomProcessor(a							,	a    )

																			with tempfile.TemporaryDirectory() as tmp_dir:
																									processor.save_pretrained(a    )
																									SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     Union[str, Any]    )	->int:



													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						False







													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						False







													class    _a     (				lowercase__							):






																				"""simple docstring"""

																				snake_case_							 =						"AutoFeatureExtractor"
																				snake_case_							 =						"AutoTokenizer"
																				snake_case_							 =						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.
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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.
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					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.
																			SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     Optional[Any]    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert"    )
													self.assertEqual(processor.__class__.__name__							,	"BertTokenizerFast"    )






							def      A_  (      self				:     Dict    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext"    )
													self.assertEqual(processor.__class__.__name__							,	"ConvNextImageProcessor"    )







@is_staging_test
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]

							@classmethod
							def      A_  (      cls				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					TOKEN
													HfFolder.save_token(a    )

							@classmethod
							def      A_  (      cls				:     List[str]    )	->Optional[int]:
													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      A_  (      self				:     Dict    )	->Dict:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					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    )

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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      A_  (      self				:     List[str]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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"							,	)

																			SCREAMING_SNAKE_CASE__     :				Dict   =					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      A_  (      self				:     Any    )	->int:
													CustomFeatureExtractor.register_for_auto_class()
													CustomTokenizer.register_for_auto_class()
													CustomProcessor.register_for_auto_class()

													SCREAMING_SNAKE_CASE__     :				Any   =					CustomFeatureExtractor.from_pretrained(a    )

													with tempfile.TemporaryDirectory() as tmp_dir:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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]    )    )
																			SCREAMING_SNAKE_CASE__     :				str   =					CustomTokenizer(a    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					CustomProcessor(a							,	a    )

													with tempfile.TemporaryDirectory() as tmp_dir:
																			create_repo(f"""{USER}/test-dynamic-processor"""							,	token=self._token    )
																			SCREAMING_SNAKE_CASE__     :				str   =					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:
																									SCREAMING_SNAKE_CASE__     :				str   =					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()

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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"    )






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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  PreTrainedTokenizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  Optional[int] = None			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					{}

						if train_file is not None:
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[train_file]
						if eval_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[eval_file]
						if test_file is not None:
												SCREAMING_SNAKE_CASE__     :				int   =					[test_file]

						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					datasets.load_dataset("csv"			,   data_files=_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					list(ds[list(files.keys()     )[0]].features.keys()     )
						SCREAMING_SNAKE_CASE__     :				int   =					features_name.pop(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					list(set(ds[list(files.keys()     )[0]][label_name]     )     )
						SCREAMING_SNAKE_CASE__     :				List[str]   =					{label: i for i, label in enumerate(_lowerCamelCase     )}
						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					tokenizer.model_input_names
						SCREAMING_SNAKE_CASE__     :				Any   =					{}

						if len(_lowerCamelCase     ) == 1:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        example[features_name[0]]			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"     )			,   batched=_lowerCamelCase			,   )
						elif len(_lowerCamelCase     ) == 2:
												for k in files.keys():
																		SCREAMING_SNAKE_CASE__     :				Any   =					ds[k].map(
																		    lambda _lowerCamelCase     : tokenizer.batch_encode_plus(
																		        (example[features_name[0]], example[features_name[1]])			,   truncation=_lowerCamelCase			,   max_length=_lowerCamelCase			,   padding="max_length"			,   )			,   batched=_lowerCamelCase			,   )

						def gen_train():
												for ex in transformed_ds[datasets.Split.TRAIN]:
																		SCREAMING_SNAKE_CASE__     :				Tuple   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_val():
												for ex in transformed_ds[datasets.Split.VALIDATION]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[int]   =					labelaid[ex[label_name]]
																		yield (d, label)

						def gen_test():
												for ex in transformed_ds[datasets.Split.TEST]:
																		SCREAMING_SNAKE_CASE__     :				int   =					{k: v for k, v in ex.items() if k in input_names}
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					labelaid[ex[label_name]]
																		yield (d, label)

						SCREAMING_SNAKE_CASE__     :				Tuple   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TRAIN in transformed_ds
						    else None
						)

						if train_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Any   =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.VALIDATION in transformed_ds
						    else None
						)

						if val_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]     )     )     )

						SCREAMING_SNAKE_CASE__     :				Dict   =					(
						    tf.data.Dataset.from_generator(
						        _lowerCamelCase			,   ({k: tf.intaa for k in input_names}, tf.intaa)			,   ({k: tf.TensorShape([None]     ) for k in input_names}, tf.TensorShape([]     ))			,   )
						    if datasets.Split.TEST in transformed_ds
						    else None
						)

						if test_ds is not None:
												SCREAMING_SNAKE_CASE__     :				Dict   =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]     )     )     )

						return train_ds, val_ds, test_ds, labelaid


__lowercase       :List[Any]       =  logging.getLogger(__name__)



@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(metadata={"help": "Which column contains the label"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the training file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the development file"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "The path of the test file"}							)
							snake_case_							 =						field(
							    default=1_28					,							metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }					,							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Overwrite the cached training and evaluation sets"}							)







@dataclass
class    _a     :






							"""simple docstring"""

							snake_case_							 =						field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							snake_case_							 =						field(default=lowercase__					,							metadata={"help": "Set this flag to use fast tokenization."}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							snake_case_							 =						field(
							    default=lowercase__					,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}					,							)







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					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			,   )
						logger.info(
						    f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1     )}, """
						    f"""16-bits training: {training_args.fpaa}"""     )
						logger.info(f"""Training/evaluation parameters {training_args}"""     )

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

						SCREAMING_SNAKE_CASE__     :				Any   =					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			,   )

						SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					get_tfds(
						    train_file=data_args.train_file			,   eval_file=data_args.dev_file			,   test_file=data_args.test_file			,   tokenizer=_lowerCamelCase			,   label_column_id=data_args.label_column_id			,   max_seq_length=data_args.max_seq_length			,   )

						SCREAMING_SNAKE_CASE__     :				str   =					AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,   num_labels=len(_lowerCamelCase     )			,   labelaid=_lowerCamelCase			,   idalabel={id: label for label, id in labelaid.items()}			,   finetuning_task="text-classification"			,   cache_dir=model_args.cache_dir			,   )

						with training_args.strategy.scope():
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					TFAutoModelForSequenceClassification.from_pretrained(
												    model_args.model_name_or_path			,   from_pt=bool(".bin" in model_args.model_name_or_path     )			,   config=_lowerCamelCase			,   cache_dir=model_args.cache_dir			,   )

						def compute_metrics(_lowerCamelCase      :  EvalPrediction     ) -> Dict:
												SCREAMING_SNAKE_CASE__     :				Dict   =					np.argmax(p.predictions			,   axis=1     )

												return {"acc": (preds == p.label_ids).mean()}

						# Initialize our Trainer
						SCREAMING_SNAKE_CASE__     :				str   =					TFTrainer(
						    model=_lowerCamelCase			,   args=_lowerCamelCase			,   train_dataset=_lowerCamelCase			,   eval_dataset=_lowerCamelCase			,   compute_metrics=_lowerCamelCase			,   )

						# Training
						if training_args.do_train:
												trainer.train()
												trainer.save_model()
												tokenizer.save_pretrained(training_args.output_dir     )

						# Evaluation
						SCREAMING_SNAKE_CASE__     :				Dict   =					{}
						if training_args.do_eval:
												logger.info("*** Evaluate ***"     )
												SCREAMING_SNAKE_CASE__     :				str   =					trainer.evaluate()
												SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					os.path.join(training_args.output_dir			,   "eval_results.txt"     )

												with open(_lowerCamelCase			,   "w"     ) as writer:
																		logger.info("***** Eval results *****"     )

																		for key, value in result.items():
																								logger.info(f"""  {key} = {value}"""     )
																								writer.write(f"""{key} = {value}\n"""     )

																		results.update(_lowerCamelCase     )

						return results


if __name__ == "__main__":
				main()