




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								=[]
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
				        F"stage{idx}.patch_embed.proj.weight",
				    )   )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
				        F"stage{idx}.patch_embed.proj.bias",
				    )   )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
				        F"stage{idx}.patch_embed.norm.weight",
				    )   )
				embed.append(
				    (
				        F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
				        F"stage{idx}.patch_embed.norm.bias",
				    )   )
				return embed




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Any								=[]
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
				        F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
				        F"stage{idx}.blocks.{cnt}.attn.proj.weight",
				    )   )
				attention_weights.append(
				    (
				        F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
				        F"stage{idx}.blocks.{cnt}.attn.proj.bias",
				    )   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight")   )
				attention_weights.append(
				    (F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias")   )
				return attention_weights




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	List[str]								=[]
				token.append((F"cvt.encoder.stages.{idx}.cls_token", """stage2.cls_token""")   )
				return token




def 		lowerCAmelCase_   (	):
				__magic_name__	:	Tuple								=[]
				head.append(("""layernorm.weight""", """norm.weight""")   )
				head.append(("""layernorm.bias""", """norm.bias""")   )
				head.append(("""classifier.weight""", """head.weight""")   )
				head.append(("""classifier.bias""", """head.bias""")   )
				return head




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase   ):
				__magic_name__	:	Optional[int]								="""imagenet-1k-id2label.json"""
				__magic_name__	:	List[Any]								=1000

				__magic_name__	:	List[str]								="""huggingface/label-files"""
				__magic_name__	:	int								=num_labels
				__magic_name__	:	List[Any]								=json.load(open(cached_download(hf_hub_url(lowerCamelCase	,     lowerCamelCase	,     repo_type="""dataset"""   )   )	,     """r"""   )   )
				__magic_name__	:	List[str]								={int(lowerCamelCase   ): v for k, v in idalabel.items()}

				__magic_name__	:	List[str]								=idalabel
				__magic_name__	:	Tuple								={v: k for k, v in idalabel.items()}

				__magic_name__	:	List[str]								=CvtConfig(num_labels=lowerCamelCase	,     idalabel=lowerCamelCase	,     labelaid=lowerCamelCase   )

				# For depth size 13 (13 = 1+2+10)
				if cvt_model.rsplit("""/"""	,     1   )[-1][4:6] == "13":
								__magic_name__	:	List[str]								=[1, 2, 10]

				# For depth size 21 (21 = 1+4+16)
				elif cvt_model.rsplit("""/"""	,     1   )[-1][4:6] == "21":
								__magic_name__	:	Union[str, Any]								=[1, 4, 16]

				# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
				else:
								__magic_name__	:	str								=[2, 2, 20]
								__magic_name__	:	Any								=[3, 12, 16]
								__magic_name__	:	Optional[Any]								=[192, 768, 1024]

				__magic_name__	:	Optional[Any]								=CvtForImageClassification(lowerCamelCase   )
				__magic_name__	:	int								=AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""   )
				__magic_name__	:	Dict								=image_size
				__magic_name__	:	Optional[Any]								=torch.load(lowerCamelCase	,     map_location=torch.device("""cpu"""   )   )

				__magic_name__	:	List[Any]								=OrderedDict()
				__magic_name__	:	Tuple								=[]

				for idx in range(len(config.depth   )   ):
								if config.cls_token[idx]:
												__magic_name__	:	List[Any]								=list_of_state_dict + cls_token(lowerCamelCase   )
								__magic_name__	:	List[Any]								=list_of_state_dict + embeddings(lowerCamelCase   )
								for cnt in range(config.depth[idx]   ):
												__magic_name__	:	Union[str, Any]								=list_of_state_dict + attention(lowerCamelCase	,     lowerCamelCase   )

				__magic_name__	:	List[str]								=list_of_state_dict + final()
				for gg in list_of_state_dict:
								print(lowerCamelCase   )
				for i in range(len(lowerCamelCase   )   ):
								__magic_name__	:	Dict								=original_weights[list_of_state_dict[i][1]]

				model.load_state_dict(lowerCamelCase   )
				model.save_pretrained(lowerCamelCase   )
				image_processor.save_pretrained(lowerCamelCase   )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
			UpperCAmelCase_		:				Optional[Any]									=     argparse.ArgumentParser()
			parser.add_argument(
			    "--cvt_model",
			    default="cvt-w24",
			    type=str,
			    help="Name of the cvt model you'd like to convert.",
			)
			parser.add_argument(
			    "--image_size",
			    default=384,
			    type=int,
			    help="Input Image Size",
			)
			parser.add_argument(
			    "--cvt_file_name",
			    default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
			    type=str,
			    help="Input Image Size",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
			)

			UpperCAmelCase_		:				Optional[int]									=     parser.parse_args()
			convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)










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_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase = None	,     ):
				__magic_name__	:	Optional[int]								={}

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

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

				if len(lowerCamelCase   ) == 1:
								for k in files.keys():
												__magic_name__	:	Dict								=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():
												__magic_name__	:	Optional[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]:
												__magic_name__	:	Any								={k: v for k, v in ex.items() if k in input_names}
												__magic_name__	:	Any								=labelaid[ex[label_name]]
												yield (d, label)

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

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

				__magic_name__	:	Union[str, 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.TRAIN in transformed_ds
				    else None
				)

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

				__magic_name__	:	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:
								__magic_name__	:	Any								=val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]   )   )   )

				__magic_name__	:	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.TEST in transformed_ds
				    else None
				)

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

				return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase_		:				int									=     logging.getLogger(__name__)




@dataclass
class 		__A      :
						UpperCamelCase     						=					field(metadata={"""help""": """Which column contains the label"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the training file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the development file"""}			)
						UpperCamelCase     						=					field(default=UpperCamelCase__					,						metadata={"""help""": """The path of the test file"""}			)
						UpperCamelCase     						=					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."""
						        )
						    }					,						)
						UpperCamelCase     						=					field(
						    default=UpperCamelCase__					,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)




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




def 		lowerCAmelCase_   (	):
				# 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.
				__magic_name__	:	List[Any]								=HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)   )
				__magic_name__				, __magic_name__				, __magic_name__	:	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.

				__magic_name__	:	Dict								=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	,     )

				__magic_name__				, __magic_name__				, __magic_name__				, __magic_name__	:	Optional[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	,     )

				__magic_name__	:	Any								=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():
								__magic_name__	:	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   ) -> Dict:
								__magic_name__	:	Tuple								=np.argmax(p.predictions	,     axis=1   )

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

				# Initialize our Trainer
				__magic_name__	:	int								=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
				__magic_name__	:	List[str]								={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""   )
								__magic_name__	:	List[str]								=trainer.evaluate()
								__magic_name__	:	Optional[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()




