



'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
							from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor

class 	__magic_name__						:







							def __init__(							self					:       Union[str, Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Tuple=2	,_UpperCAmelCase					:       Optional[int]=32	,_UpperCAmelCase					:       Any=16	,_UpperCAmelCase					:       Tuple=3	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Union[str, Any]=32	,_UpperCAmelCase					:       Optional[int]=4	,_UpperCAmelCase					:       str=[0, 1, 2, 3]	,_UpperCAmelCase					:       Optional[Any]=4	,_UpperCAmelCase					:       int=37	,_UpperCAmelCase					:       int="gelu"	,_UpperCAmelCase					:       Tuple=0.1	,_UpperCAmelCase					:       Dict=0.1	,_UpperCAmelCase					:       List[Any]=0.02	,_UpperCAmelCase					:       str=3	,_UpperCAmelCase					:       Dict=[1, 384, 24, 24]	,_UpperCAmelCase					:       str=True	,_UpperCAmelCase					:       Any=None	,):
									_a     :      Optional[Any]        =							parent
									_a     :      Any        =							batch_size
									_a     :      str        =							image_size
									_a     :      Any        =							patch_size
									_a     :      Dict        =							num_channels
									_a     :      int        =							is_training
									_a     :      str        =							use_labels
									_a     :      List[Any]        =							hidden_size
									_a     :      Dict        =							num_hidden_layers
									_a     :      int        =							backbone_out_indices
									_a     :      Any        =							num_attention_heads
									_a     :      Dict        =							intermediate_size
									_a     :      Dict        =							hidden_act
									_a     :      Optional[int]        =							hidden_dropout_prob
									_a     :      Dict        =							attention_probs_dropout_prob
									_a     :      Union[str, Any]        =							initializer_range
									_a     :      Tuple        =							num_labels
									_a     :      Any        =							backbone_featmap_shape
									_a     :      Any        =							scope
									_a     :      Dict        =							is_hybrid
									# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
									_a     :      List[str]        =							(image_size // patch_size) ** 2
									_a     :      Tuple        =							num_patches + 1







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Dict        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_a     :      Dict        =							None
									if self.use_labels:
											_a     :      Optional[int]        =							ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels		)

									_a     :      Any        =							self.get_config()

									return config, pixel_values, labels







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Union[str, Any]        =							{
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									    'hidden_sizes': [96, 192, 384, 768],
									    'num_groups': 2,
									}

									return DPTConfig(
									    image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,backbone_out_indices=self.backbone_out_indices	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,is_decoder=_UpperCAmelCase	,initializer_range=self.initializer_range	,is_hybrid=self.is_hybrid	,backbone_config=_UpperCAmelCase	,backbone_featmap_shape=self.backbone_featmap_shape	,)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Tuple        =							DPTModel(config=_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Optional[int]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Union[str, Any]		):
									_a     :      Dict        =							self.num_labels
									_a     :      List[Any]        =							DPTForDepthEstimation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Union[str, Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       str		):
									_a     :      Union[str, Any]        =							self.num_labels
									_a     :      Union[str, Any]        =							DPTForSemanticSegmentation(_UpperCAmelCase		)
									model.to(_UpperCAmelCase		)
									model.eval()
									_a     :      Tuple        =							model(_UpperCAmelCase	,labels=_UpperCAmelCase		)
									self.parent.assertEqual(
									    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size, self.image_size)		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Dict        =							self.prepare_config_and_inputs()
									_a       ,					_a       ,					_a     :      Any        =							config_and_inputs
									_a     :      Optional[Any]        =							{'pixel_values': pixel_values}
									return config, inputs_dict

@require_torch
class 	__magic_name__						(    _UpperCamelCase			,						_UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    List[Any]   				=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							lowerCAmelCase       :    Tuple   				=					(
							    {
							        'depth-estimation': DPTForDepthEstimation,
							        'feature-extraction': DPTModel,
							        'image-segmentation': DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Any   				=					False
							lowerCAmelCase       :    Optional[Any]   				=					False







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Union[str, Any]        =							DPTModelTester(self		)
									_a     :      List[Any]        =							ConfigTester(self	,config_class=_UpperCAmelCase	,has_text_modality=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Dict		):
									self.config_tester.run_common_tests()







							@unittest.skip(reason='DPT does not use inputs_embeds'		)
							def 							__lowercase			(							self					:       List[Any]		):
									pass







							def 							__lowercase			(							self					:       str		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      str        =							model_class(_UpperCAmelCase		)
											self.assertIsInstance(model.get_input_embeddings()	,(nn.Module)		)
											_a     :      List[Any]        =							model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_UpperCAmelCase	,nn.Linear		)		)







							def 							__lowercase			(							self					:       Dict		):
									_a       ,					_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a     :      str        =							model_class(_UpperCAmelCase		)
											_a     :      List[str]        =							inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a     :      str        =							[*signature.parameters.keys()]

											_a     :      Optional[Any]        =							['pixel_values']
											self.assertListEqual(arg_names[:1]	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      List[str]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      List[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a       ,					_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
											_a     :      Optional[Any]        =							True

											if model_class in get_values(_UpperCAmelCase		):
													continue

											_a     :      List[Any]        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.train()
											_a     :      int        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      str        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       Optional[Any]		):
									for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
													continue

											_a       ,					_a     :      Tuple        =							self.model_tester.prepare_config_and_inputs_for_common()
											_a     :      Optional[int]        =							False
											_a     :      Optional[int]        =							True

											if model_class in get_values(_UpperCAmelCase		) or not model_class.supports_gradient_checkpointing:
													continue
											_a     :      Any        =							model_class(_UpperCAmelCase		)
											model.to(_UpperCAmelCase		)
											model.gradient_checkpointing_enable()
											model.train()
											_a     :      Optional[Any]        =							self._prepare_for_class(_UpperCAmelCase	,_UpperCAmelCase	,return_labels=_UpperCAmelCase		)
											_a     :      str        =							model(**_UpperCAmelCase		).loss
											loss.backward()







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a       ,					_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

									_a     :      List[str]        =							_config_zero_init(_UpperCAmelCase		)
									for model_class in self.all_model_classes:
											_a     :      Optional[Any]        =							model_class(config=_UpperCAmelCase		)
											# Skip the check for the backbone
											_a     :      Optional[Any]        =							[]
											for name, module in model.named_modules():
													if module.__class__.__name__ == "DPTViTHybridEmbeddings":
															_a     :      int        =							[F"""{name}.{key}""" for key in module.state_dict().keys()]
															break

											for name, param in model.named_parameters():
													if param.requires_grad:
															if name in backbone_params:
																	continue
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item()	,[0.0, 1.0]	,msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""	,)







							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
							def 							__lowercase			(							self					:       Tuple		):
									pass







							@slow
							def 							__lowercase			(							self					:       Tuple		):
									for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											_a     :      int        =							DPTModel.from_pretrained(_UpperCAmelCase		)
											self.assertIsNotNone(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       str		):
									# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
									_a       ,					_a     :      Any        =							self.model_tester.prepare_config_and_inputs_for_common()
									_a     :      int        =							'add'
									with self.assertRaises(_UpperCAmelCase		):
											_a     :      Dict        =							DPTForDepthEstimation(_UpperCAmelCase		)



def      __lowerCamelCase     (      )			->      Tuple:
		_a     :      List[Any]        =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
		return image

@require_torch
@require_vision
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       str		):
									_a     :      Optional[int]        =							DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'		)
									_a     :      List[Any]        =							DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'		).to(_UpperCAmelCase		)

									_a     :      Optional[int]        =							prepare_img()
									_a     :      str        =							image_processor(images=_UpperCAmelCase	,return_tensors='pt'		).to(_UpperCAmelCase		)

									# forward pass
									with torch.no_grad():
											_a     :      int        =							model(**_UpperCAmelCase		)
											_a     :      Union[str, Any]        =							outputs.predicted_depth

									# verify the predicted depth
									_a     :      List[str]        =							torch.Size((1, 384, 384)		)
									self.assertEqual(predicted_depth.shape	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							torch.tensor(
									    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]		).to(_UpperCAmelCase		)

									self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100	,_UpperCAmelCase	,atol=1E-4		)		)











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_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			PreTrainedTokenizer		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[int] = None		,   )      ->	List[Any]:
    UpperCamelCase__							:				Dict          =      {}

    if train_file is not None:
        UpperCamelCase__							:				str          =      [train_file]
    if eval_file is not None:
        UpperCamelCase__							:				Union[str, Any]          =      [eval_file]
    if test_file is not None:
        UpperCamelCase__							:				Tuple          =      [test_file]

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

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

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

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

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

    UpperCamelCase__							:				Tuple          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Optional[Any]          =      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

    UpperCamelCase__							:				int          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Dict          =      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

    UpperCamelCase__							:				Optional[Any]          =      (
        tf.data.Dataset.from_generator(
            __UpperCAmelCase		,   ({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:
        UpperCamelCase__							:				Union[str, Any]          =      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     lowercase__       :

   '''simple docstring'''


   a						:							int   		=		field(metadata={"help": "Which column contains the label"}				)
   a						:							str   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the training file"}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the development file"}				)
   a						:							Optional[str]   		=		field(default=__lowerCamelCase			,				metadata={"help": "The path of the test file"}				)
   a						:							int   		=		field(
       default=128			,				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=__lowerCamelCase			,				metadata={"help": "Overwrite the cached training and evaluation sets"}				)






@dataclass
class     lowercase__       :

   '''simple docstring'''


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




def 		lowerCAmelCase_    (  )      ->	int:
    # 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.
    UpperCamelCase__							:				Tuple          =      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				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.

    UpperCamelCase__							:				List[str]          =      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		,   )

    UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      get_tfds(
        train_file=data_args.train_file		,   eval_file=data_args.dev_file		,   test_file=data_args.test_file		,   tokenizer=__UpperCAmelCase		,   label_column_id=data_args.label_column_id		,   max_seq_length=data_args.max_seq_length		,   )

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

    with training_args.strategy.scope():
        UpperCamelCase__							:				str          =      TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path		,   from_pt=bool('''.bin''' in model_args.model_name_or_path      )		,   config=__UpperCAmelCase		,   cache_dir=model_args.cache_dir		,   )

    def compute_metrics(__UpperCAmelCase:			EvalPrediction      ) -> Dict:
        UpperCamelCase__							:				Optional[Any]          =      np.argmax(p.predictions		,   axis=1      )

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

    # Initialize our Trainer
    UpperCamelCase__							:				Union[str, Any]          =      TFTrainer(
        model=__UpperCAmelCase		,   args=__UpperCAmelCase		,   train_dataset=__UpperCAmelCase		,   eval_dataset=__UpperCAmelCase		,   compute_metrics=__UpperCAmelCase		,   )

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

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

        with open(__UpperCAmelCase		,   '''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(__UpperCAmelCase      )

    return results


if __name__ == "__main__":
  main()

