
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			[]

				# fmt: off
				# stem:
				rename_keys.append(("cls_token", "vit.embeddings.cls_token")    )
				rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")    )

				rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")    )
				rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")    )

				# backbone
				rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")    )
				rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")    )
				rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")    )

				for stage_idx in range(len(config.backbone_config.depths    )    ):
								for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight')    )
												rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias')    )

								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight')    )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight')    )
								rename_keys.append((F'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', F'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias')    )

				# transformer encoder
				for i in range(config.num_hidden_layers    ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight')    )
								rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias')    )
								rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight')    )
								rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias')    )
								rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight')    )
								rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias')    )
								rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight')    )
								rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias')    )
								rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight')    )
								rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias')    )

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								        ("pre_logits.fc.weight", "pooler.dense.weight"),
								        ("pre_logits.fc.bias", "pooler.dense.bias"),
								    ]    )

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowerCamelCase_						=			[(pair[0], pair[1][4:]) if pair[1].startswith("vit"    ) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]    )
				# fmt: on

				return rename_keys





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False    ):
				for i in range(config.num_hidden_layers    ):
								if base_model:
												lowerCamelCase_						=			""
								else:
												lowerCamelCase_						=			"vit."
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase_						=			in_proj_weight[
								    : config.hidden_size, :
								]
								lowerCamelCase_						=			in_proj_bias[: config.hidden_size]
								lowerCamelCase_						=			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase_						=			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase_						=			in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCamelCase_						=			in_proj_bias[-config.hidden_size :]





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__lowerCamelCase	, __lowerCamelCase    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			dct.pop(__lowerCamelCase    )
				lowerCamelCase_						=			val





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_						=			Image.open(requests.get(__lowerCamelCase	, stream=__lowerCamelCase    ).raw    )
				return im





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False    ):
				lowerCamelCase_						=			BitConfig(
				    global_padding="same"	, layer_type="bottleneck"	, depths=(3, 4, 9)	, out_features=["stage3"]	, embedding_dynamic_padding=__lowerCamelCase	, )
				lowerCamelCase_						=			ViTHybridConfig(backbone_config=__lowerCamelCase	, image_size=3_8_4	, num_labels=1_0_0_0    )
				lowerCamelCase_						=			False

				# load original model from timm
				lowerCamelCase_						=			timm.create_model(__lowerCamelCase	, pretrained=__lowerCamelCase    )
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				lowerCamelCase_						=			timm_model.state_dict()
				if base_model:
								remove_classification_head_(__lowerCamelCase    )
				lowerCamelCase_						=			create_rename_keys(__lowerCamelCase	, __lowerCamelCase    )
				for src, dest in rename_keys:
								rename_key(__lowerCamelCase	, __lowerCamelCase	, __lowerCamelCase    )
				read_in_q_k_v(__lowerCamelCase	, __lowerCamelCase	, __lowerCamelCase    )

				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"imagenet-1k-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(__lowerCamelCase	, __lowerCamelCase	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowerCamelCase_						=			ViTHybridModel(__lowerCamelCase    ).eval()
				else:
								lowerCamelCase_						=			ViTHybridForImageClassification(__lowerCamelCase    ).eval()
				model.load_state_dict(__lowerCamelCase    )

				# create image processor
				lowerCamelCase_						=			create_transform(**resolve_data_config({}	, model=__lowerCamelCase    )    )
				lowerCamelCase_						=			transform.transforms

				lowerCamelCase_						=			{
				    "bilinear": PILImageResampling.BILINEAR,
				    "bicubic": PILImageResampling.BICUBIC,
				    "nearest": PILImageResampling.NEAREST,
				}

				lowerCamelCase_						=			ViTHybridImageProcessor(
				    do_resize=__lowerCamelCase	, size={"shortest_edge": timm_transforms[0].size}	, resample=pillow_resamplings[timm_transforms[0].interpolation.value]	, do_center_crop=__lowerCamelCase	, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}	, do_normalize=__lowerCamelCase	, image_mean=timm_transforms[-1].mean.tolist()	, image_std=timm_transforms[-1].std.tolist()	, )

				lowerCamelCase_						=			prepare_img()
				lowerCamelCase_						=			transform(__lowerCamelCase    ).unsqueeze(0    )
				lowerCamelCase_						=			processor(__lowerCamelCase	, return_tensors="pt"    ).pixel_values

				# verify pixel values
				assert torch.allclose(__lowerCamelCase	, __lowerCamelCase    )

				# verify logits
				with torch.no_grad():
								lowerCamelCase_						=			model(__lowerCamelCase    )
								lowerCamelCase_						=			outputs.logits

				print("Predicted class:"	, logits.argmax(-1    ).item()    )
				if base_model:
								lowerCamelCase_						=			timm_model.forward_features(__lowerCamelCase    )
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(__lowerCamelCase	, outputs.pooler_output	, atol=1e-3    )
				else:
								lowerCamelCase_						=			timm_model(__lowerCamelCase    )
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(__lowerCamelCase	, outputs.logits	, atol=1e-3    )
				print("Looks ok!"    )

				if pytorch_dump_folder_path is not None:
								Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )
								print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'    )
								model.save_pretrained(__lowerCamelCase    )
								print(F'Saving processor to {pytorch_dump_folder_path}'    )
								processor.save_pretrained(__lowerCamelCase    )

				if push_to_hub:
								print(F'Pushing model and processor to the hub {vit_name}'    )
								model.push_to_hub(F'ybelkada/{vit_name}'    )
								processor.push_to_hub(F'ybelkada/{vit_name}'    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--vit_name''',
			    default='''vit_base_r50_s16_384''',
			    type=str,
			    help='''Name of the hybrid ViT timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
			)

			__A				=parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




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				,					):
       snake_case       :   int							=      {}

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

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

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      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():
                     snake_case       :   List[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]:
                     snake_case       :   Dict							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Union[str, Any]							=      labelaid[ex[label_name]]
                     yield (d, label)

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

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

       snake_case       :   int							=      (
           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:
              snake_case       :   Optional[Any]							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       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.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              snake_case       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           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:
              snake_case       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,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=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






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



def 			UpperCamelCase					(    ):
       # 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.
       snake_case       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   int							=      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.

       snake_case       :   Tuple							=      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				,					)

       snake_case				,					snake_case				,					snake_case				,					snake_case       :   Tuple							=      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				,					)

       snake_case       :   Optional[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():
              snake_case       :   int							=      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:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

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

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





