


"""simple docstring"""



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetaImageProcessor
class 					__magic_name__		(			unittest.TestCase     ):






       def __init__(   self  :    str  ,     snake_case__  :    Dict  ,     snake_case__  :    Dict=7  ,     snake_case__  :    Dict=3  ,     snake_case__  :    List[str]=3_0  ,     snake_case__  :    List[str]=4_0_0  ,     snake_case__  :    Tuple=True  ,     snake_case__  :    Any=None  ,     snake_case__  :    str=True  ,     snake_case__  :    Optional[int]=[0.5, 0.5, 0.5]  ,     snake_case__  :    Union[str, Any]=[0.5, 0.5, 0.5]  ,     snake_case__  :    Optional[Any]=True  ,     snake_case__  :    Dict=1 / 2_5_5  ,     snake_case__  :    Union[str, Any]=True  ,     ):

              '''simple docstring'''






              lowercase		:Any       							=				size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
              lowercase		:Optional[Any]       							=				parent
              lowercase		:Dict       							=				batch_size
              lowercase		:Optional[Any]       							=				num_channels
              lowercase		:Union[str, Any]       							=				min_resolution
              lowercase		:List[Any]       							=				max_resolution
              lowercase		:Union[str, Any]       							=				do_resize
              lowercase		:Optional[Any]       							=				size
              lowercase		:Optional[Any]       							=				do_normalize
              lowercase		:Any       							=				image_mean
              lowercase		:Any       							=				image_std
              lowercase		:Any       							=				do_rescale
              lowercase		:Optional[Any]       							=				rescale_factor
              lowercase		:Dict       							=				do_pad






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
                  "do_rescale": self.do_rescale,
                  "rescale_factor": self.rescale_factor,
                  "do_pad": self.do_pad,
              }







       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    str  ,     snake_case__  :    Any=False				):

              '''simple docstring'''






              if not batched:
                     lowercase		:Any       							=				image_inputs[0]
                     if isinstance(snake_case__  ,     Image.Image				):
                            lowercase							,  lowercase		:Union[str, Any]       							=				image.size
                     else:
                            lowercase							,  lowercase		:Tuple       							=				image.shape[1], image.shape[2]
                     if w < h:
                            lowercase		:int       							=				int(self.size['''shortest_edge'''] * h / w				)
                            lowercase		:Optional[Any]       							=				self.size['''shortest_edge''']
                     elif w > h:
                            lowercase		:List[str]       							=				self.size['''shortest_edge''']
                            lowercase		:str       							=				int(self.size['''shortest_edge'''] * w / h				)
                     else:
                            lowercase		:List[str]       							=				self.size['''shortest_edge''']
                            lowercase		:int       							=				self.size['''shortest_edge''']

              else:
                     lowercase		:Union[str, Any]       							=				[]
                     for image in image_inputs:
                            lowercase							,  lowercase		:Any       							=				self.get_expected_values([image]				)
                            expected_values.append((expected_height, expected_width)				)
                     lowercase		:List[str]       							=				max(snake_case__  ,     key=lambda snake_case__				: item[0]				)[0]
                     lowercase		:Any       							=				max(snake_case__  ,     key=lambda snake_case__				: item[1]				)[1]

              return expected_height, expected_width







@require_torch
@require_vision
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Any        =   DetaImageProcessor if is_vision_available() else None






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				DetaImageProcessingTester(self				)






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return self.image_processor_tester.prepare_image_processor_dict()






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				self.image_processing_class(**self.image_processor_dict				)
              self.assertTrue(hasattr(snake_case__  ,     '''image_mean'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''image_std'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_normalize'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_resize'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_rescale'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''do_pad'''				)				)
              self.assertTrue(hasattr(snake_case__  ,     '''size'''				)				)






       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Dict       							=				self.image_processing_class.from_dict(self.image_processor_dict				)
              self.assertEqual(image_processor.size  ,     {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}				)
              self.assertEqual(image_processor.do_pad  ,     snake_case__				)






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:List[str]       							=				self.image_processing_class(**self.image_processor_dict				)
              # create random PIL images
              lowercase		:Optional[Any]       							=				prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__				)
              for image in image_inputs:
                     self.assertIsInstance(snake_case__  ,     Image.Image				)

              # Test not batched input
              lowercase		:Tuple       							=				image_processing(image_inputs[0]  ,     return_tensors='''pt'''				).pixel_values

              lowercase							,  lowercase		:Any       							=				self.image_processor_tester.get_expected_values(snake_case__				)

              self.assertEqual(
                  encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

              # Test batched
              lowercase							,  lowercase		:str       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              lowercase		:Optional[Any]       							=				image_processing(snake_case__  ,     return_tensors='''pt'''				).pixel_values
              self.assertEqual(
                  encoded_images.shape  ,     (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )  ,     )






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.image_processing_class(**self.image_processor_dict				)
              # create random numpy tensors
              lowercase		:str       							=				prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__  ,     numpify=snake_case__				)
              for image in image_inputs:
                     self.assertIsInstance(snake_case__  ,     np.ndarray				)

              # Test not batched input
              lowercase		:Any       							=				image_processing(image_inputs[0]  ,     return_tensors='''pt'''				).pixel_values

              lowercase							,  lowercase		:Optional[int]       							=				self.image_processor_tester.get_expected_values(snake_case__				)

              self.assertEqual(
                  encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

              # Test batched
              lowercase		:Optional[int]       							=				image_processing(snake_case__  ,     return_tensors='''pt'''				).pixel_values

              lowercase							,  lowercase		:List[str]       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              self.assertEqual(
                  encoded_images.shape  ,     (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )  ,     )






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:int       							=				self.image_processing_class(**self.image_processor_dict				)
              # create random PyTorch tensors
              lowercase		:Union[str, Any]       							=				prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=snake_case__  ,     torchify=snake_case__				)
              for image in image_inputs:
                     self.assertIsInstance(snake_case__  ,     torch.Tensor				)

              # Test not batched input
              lowercase		:Dict       							=				image_processing(image_inputs[0]  ,     return_tensors='''pt'''				).pixel_values

              lowercase							,  lowercase		:Optional[Any]       							=				self.image_processor_tester.get_expected_values(snake_case__				)

              self.assertEqual(
                  encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

              # Test batched
              lowercase		:Dict       							=				image_processing(snake_case__  ,     return_tensors='''pt'''				).pixel_values

              lowercase							,  lowercase		:Optional[int]       							=				self.image_processor_tester.get_expected_values(snake_case__  ,     batched=snake_case__				)

              self.assertEqual(
                  encoded_images.shape  ,     (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      expected_height,
                      expected_width,
                  )  ,     )






       @slow
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''  ,     '''r'''				) as f:
                     lowercase		:Dict       							=				json.loads(f.read()				)

              lowercase		:Union[str, Any]       							=				{'''image_id''': 3_9_7_6_9, '''annotations''': target}

              # encode them
              lowercase		:Dict       							=				DetaImageProcessor()
              lowercase		:Union[str, Any]       							=				image_processing(images=snake_case__  ,     annotations=snake_case__  ,     return_tensors='''pt'''				)

              # verify pixel values
              lowercase		:Union[str, Any]       							=				torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
              self.assertEqual(encoding['''pixel_values'''].shape  ,     snake_case__				)

              lowercase		:Optional[Any]       							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,     snake_case__  ,     atol=1e-4				)				)

              # verify area
              lowercase		:Optional[Any]       							=				torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,     snake_case__				)				)
              # verify boxes
              lowercase		:List[Any]       							=				torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,     snake_case__				)
              lowercase		:str       							=				torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,     snake_case__  ,     atol=1e-3				)				)
              # verify image_id
              lowercase		:Union[str, Any]       							=				torch.tensor([3_9_7_6_9]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,     snake_case__				)				)
              # verify is_crowd
              lowercase		:Optional[Any]       							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,     snake_case__				)				)
              # verify class_labels
              lowercase		:Union[str, Any]       							=				torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,     snake_case__				)				)
              # verify orig_size
              lowercase		:Union[str, Any]       							=				torch.tensor([4_8_0, 6_4_0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,     snake_case__				)				)
              # verify size
              lowercase		:Dict       							=				torch.tensor([8_0_0, 1_0_6_6]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,     snake_case__				)				)







       @slow
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:List[str]       							=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''  ,     '''r'''				) as f:
                     lowercase		:List[str]       							=				json.loads(f.read()				)

              lowercase		:Optional[int]       							=				{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

              lowercase		:List[Any]       							=				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''				)

              # encode them
              lowercase		:List[Any]       							=				DetaImageProcessor(format='''coco_panoptic'''				)
              lowercase		:List[Any]       							=				image_processing(images=snake_case__  ,     annotations=snake_case__  ,     masks_path=snake_case__  ,     return_tensors='''pt'''				)

              # verify pixel values
              lowercase		:Dict       							=				torch.Size([1, 3, 8_0_0, 1_0_6_6]				)
              self.assertEqual(encoding['''pixel_values'''].shape  ,     snake_case__				)

              lowercase		:Dict       							=				torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
              self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]  ,     snake_case__  ,     atol=1e-4				)				)

              # verify area
              lowercase		:Union[str, Any]       							=				torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']  ,     snake_case__				)				)
              # verify boxes
              lowercase		:Union[str, Any]       							=				torch.Size([6, 4]				)
              self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape  ,     snake_case__				)
              lowercase		:str       							=				torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]  ,     snake_case__  ,     atol=1e-3				)				)
              # verify image_id
              lowercase		:Tuple       							=				torch.tensor([3_9_7_6_9]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']  ,     snake_case__				)				)
              # verify is_crowd
              lowercase		:Tuple       							=				torch.tensor([0, 0, 0, 0, 0, 0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']  ,     snake_case__				)				)
              # verify class_labels
              lowercase		:str       							=				torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']  ,     snake_case__				)				)
              # verify masks
              lowercase		:Dict       							=				8_2_2_8_7_3
              self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()  ,     snake_case__				)
              # verify orig_size
              lowercase		:List[str]       							=				torch.tensor([4_8_0, 6_4_0]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']  ,     snake_case__				)				)
              # verify size
              lowercase		:List[str]       							=				torch.tensor([8_0_0, 1_0_6_6]				)
              self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']  ,     snake_case__				)				)





"""simple docstring"""



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       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
       lowercase		:Dict       							=				{}

       if train_file is not None:
              lowercase		:Union[str, Any]       							=				[train_file]
       if eval_file is not None:
              lowercase		:str       							=				[eval_file]
       if test_file is not None:
              lowercase		:Dict       							=				[test_file]

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

       if len(a_) == 1:
              for k in files.keys():
                     lowercase		:Dict       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             example[features_name[0]]				,				truncation=a_				,				max_length=a_				,				padding='''max_length''')				,				batched=a_				,				)
       elif len(a_) == 2:
              for k in files.keys():
                     lowercase		:Optional[Any]       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,				truncation=a_				,				max_length=a_				,				padding='''max_length'''				,				)				,				batched=a_				,				)

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

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

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

       lowercase		:List[str]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({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:
              lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

       lowercase		:Optional[int]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({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:
              lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

       lowercase		:Optional[Any]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({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:
              lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

       return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
       __A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
       __A			:      int        =   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."
               )
           }			,						)
       __A			:      bool        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







@dataclass
class 					__magic_name__		:
       __A			:      str        =   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
       __A			:      bool        =   field(default=__UpperCAmelCase			,						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.
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)







def       lowerCamelCase					()     ->							Dict:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
       lowercase							,  lowercase							,  lowercase		:Union[str, 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.

       lowercase		:Optional[int]       							=				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				,				)

       lowercase							,  lowercase							,  lowercase							,  lowercase		:int       							=				get_tfds(
           train_file=data_args.train_file				,				eval_file=data_args.dev_file				,				test_file=data_args.test_file				,				tokenizer=a_				,				label_column_id=data_args.label_column_id				,				max_seq_length=data_args.max_seq_length				,				)

       lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=len(a_)				,				labelaid=a_				,				idalabel={id: label for label, id in labelaid.items()}				,				finetuning_task='''text-classification'''				,				cache_dir=model_args.cache_dir				,				)

       with training_args.strategy.scope():
              lowercase		:int       							=				TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,				from_pt=bool('''.bin''' in model_args.model_name_or_path)				,				config=a_				,				cache_dir=model_args.cache_dir				,				)

       def compute_metrics(a_						:EvalPrediction) -> Dict:
              lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

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

       # Initialize our Trainer
       lowercase		:Any       							=				TFTrainer(
           model=a_				,				args=a_				,				train_dataset=a_				,				eval_dataset=a_				,				compute_metrics=a_				,				)

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

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

              with open(a_				,				'''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(a_)

       return results


if __name__ == "__main__":
 main()

