





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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase:	Tuple = logging.get_logger(__name__)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str   ,    _lowercase     : Optional[Any]=False			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		[]
		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"""deit.encoder.layer.{i}.layernorm_before.weight""")			)
				rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")			)
				rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")			)
				rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")			)
				rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")			)
				rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")			)
				rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")			)
				rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")			)
				rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")			)
				rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")			)

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('cls_token', 'deit.embeddings.cls_token'),
		        ('dist_token', 'deit.embeddings.distillation_token'),
		        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
		        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
		        ('pos_embed', 'deit.embeddings.position_embeddings'),
		    ]			)

		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 "deit" from all keys that start with "deit"
				lowercase__					:      Any      =		[(pair[0], pair[1][4:]) if pair[1].startswith('deit'			) else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ('norm.weight', 'deit.layernorm.weight'),
				        ('norm.bias', 'deit.layernorm.bias'),
				        ('head.weight', 'cls_classifier.weight'),
				        ('head.bias', 'cls_classifier.bias'),
				        ('head_dist.weight', 'distillation_classifier.weight'),
				        ('head_dist.bias', 'distillation_classifier.bias'),
				    ]			)

		return rename_keys


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : int   ,    _lowercase     : Any=False			)   ->							List[Any]:




		'''simple docstring'''
		for i in range(config.num_hidden_layers			):
				if base_model:
						lowercase__					:      List[Any]      =		""
				else:
						lowercase__					:      Optional[int]      =		"deit."
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowercase__					:      int      =		state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
				lowercase__					:      List[Any]      =		state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
				# next, add query, keys and values (in that order) to the state dict
				lowercase__					:      Tuple      =		in_proj_weight[
				    : config.hidden_size, :
				]
				lowercase__					:      Dict      =		in_proj_bias[: config.hidden_size]
				lowercase__					:      Optional[int]      =		in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowercase__					:      List[Any]      =		in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowercase__					:      Union[str, Any]      =		in_proj_weight[
				    -config.hidden_size :, :
				]
				lowercase__					:      List[Any]      =		in_proj_bias[-config.hidden_size :]


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Any   ,    _lowercase     : int			)   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      int      =		dct.pop(__UpperCamelCase			)
		lowercase__					:      Dict      =		val


def       SCREAMING_SNAKE_CASE__  (       )   ->							Any:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowercase__					:      Optional[int]      =		Image.open(requests.get(__UpperCamelCase   ,    stream=__UpperCamelCase			).raw			)
		return im


@torch.no_grad()
def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : int			)   ->							Union[str, Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		DeiTConfig()
		# all deit models have fine-tuned heads
		lowercase__					:      Optional[int]      =		False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		lowercase__					:      Optional[int]      =		1_000
		lowercase__					:      Union[str, Any]      =		"huggingface/label-files"
		lowercase__					:      Tuple      =		"imagenet-1k-id2label.json"
		lowercase__					:      List[Any]      =		json.load(open(hf_hub_download(__UpperCamelCase   ,    __UpperCamelCase   ,    repo_type='dataset'			)   ,    'r'			)			)
		lowercase__					:      Any      =		{int(__UpperCamelCase			): v for k, v in idalabel.items()}
		lowercase__					:      List[str]      =		idalabel
		lowercase__					:      List[Any]      =		{v: k for k, v in idalabel.items()}
		lowercase__					:      Dict      =		int(deit_name[-6:-4]			)
		lowercase__					:      str      =		int(deit_name[-3:]			)
		# size of the architecture
		if deit_name[9:].startswith('tiny'			):
				lowercase__					:      Optional[int]      =		192
				lowercase__					:      Dict      =		768
				lowercase__					:      List[Any]      =		12
				lowercase__					:      List[str]      =		3
		elif deit_name[9:].startswith('small'			):
				lowercase__					:      int      =		384
				lowercase__					:      Any      =		1_536
				lowercase__					:      Any      =		12
				lowercase__					:      List[Any]      =		6
		if deit_name[9:].startswith('base'			):
				pass
		elif deit_name[4:].startswith('large'			):
				lowercase__					:      Optional[Any]      =		1_024
				lowercase__					:      str      =		4_096
				lowercase__					:      Tuple      =		24
				lowercase__					:      Optional[Any]      =		16

		# load original model from timm
		lowercase__					:      List[Any]      =		timm.create_model(__UpperCamelCase   ,    pretrained=__UpperCamelCase			)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowercase__					:      Union[str, Any]      =		timm_model.state_dict()
		lowercase__					:      List[Any]      =		create_rename_keys(__UpperCamelCase   ,    __UpperCamelCase			)
		for src, dest in rename_keys:
				rename_key(__UpperCamelCase   ,    __UpperCamelCase   ,    __UpperCamelCase			)
		read_in_q_k_v(__UpperCamelCase   ,    __UpperCamelCase   ,    __UpperCamelCase			)

		# load HuggingFace model
		lowercase__					:      Optional[Any]      =		DeiTForImageClassificationWithTeacher(__UpperCamelCase			).eval()
		model.load_state_dict(__UpperCamelCase			)

		# Check outputs on an image, prepared by DeiTImageProcessor
		lowercase__					:      Any      =		int(
		    (256 / 224) * config.image_size			)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		lowercase__					:      Optional[int]      =		DeiTImageProcessor(size=__UpperCamelCase   ,    crop_size=config.image_size			)
		lowercase__					:      List[Any]      =		image_processor(images=prepare_img()   ,    return_tensors='pt'			)
		lowercase__					:      Dict      =		encoding["pixel_values"]
		lowercase__					:      Optional[Any]      =		model(__UpperCamelCase			)

		lowercase__					:      str      =		timm_model(__UpperCamelCase			)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__UpperCamelCase   ,    outputs.logits   ,    atol=1e-3			)

		Path(__UpperCamelCase			).mkdir(exist_ok=__UpperCamelCase			)
		print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""			)
		model.save_pretrained(__UpperCamelCase			)
		print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
		image_processor.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
			__UpperCamelCase:	Optional[Any] = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--deit_name""",
			    default="""vit_deit_base_distilled_patch16_224""",
			    type=str,
			    help="""Name of the DeiT 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."""
			)

			__UpperCamelCase:	Optional[int] = parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase		       =      logging.getLogger(__name__)


@dataclass
class 				_a						:





							'''simple docstring'''

							A				:Optional[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=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }       ,  )





@dataclass
class 				_a						:





							'''simple docstring'''

							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Train language if it is different from the evaluation language."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,  )
							A				:Optional[bool] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}       ,  )
							A				:str =       field(
							    default="main"       ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,  )






def     SCREAMING_SNAKE_CASE(      )	->						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.

				a__	:							List[str]      						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				a__						,   a__						,   a__	:							Optional[int]      						= parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_xnli"	,				__UpperCamelCase )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,				datefmt="%m/%d/%Y %H:%M:%S"	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				a__	:							int      						= training_args.get_process_log_level()
				logger.setLevel(__UpperCamelCase )
				datasets.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
				logger.info(F'Training/evaluation parameters {training_args}' )

				# Detecting last checkpoint.
				a__	:							Union[str, Any]      						= None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								a__	:							Dict      						= get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome." )
								elif last_checkpoint is not None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												a__	:							Any      						= load_dataset(
												    "xnli"	,				model_args.language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								else:
												a__	:							List[str]      						= load_dataset(
												    "xnli"	,				model_args.train_language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							List[Any]      						= train_dataset.features["label"].names

				if training_args.do_eval:
								a__	:							Tuple      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="validation"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							int      						= eval_dataset.features["label"].names

				if training_args.do_predict:
								a__	:							Dict      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="test"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							Optional[Any]      						= predict_dataset.features["label"].names

				# Labels
				a__	:							List[Any]      						= len(__UpperCamelCase )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				a__	:							List[Any]      						= AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=__UpperCamelCase	,				idalabel={str(__UpperCamelCase ): label for i, label in enumerate(__UpperCamelCase )}	,				labelaid={label: i for i, label in enumerate(__UpperCamelCase )}	,				finetuning_task="xnli"	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Union[str, Any]      						= AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,				do_lower_case=model_args.do_lower_case	,				cache_dir=model_args.cache_dir	,				use_fast=model_args.use_fast_tokenizer	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Optional[int]      						= AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path	,				from_tf=bool(".ckpt" in model_args.model_name_or_path )	,				config=__UpperCamelCase	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,				)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								a__	:							Dict      						= "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								a__	:							Optional[Any]      						= False

				def preprocess_function(__UpperCamelCase ):
								# Tokenize the texts
								return tokenizer(
								    examples["premise"]	,				examples["hypothesis"]	,				padding=__UpperCamelCase	,				max_length=data_args.max_seq_length	,				truncation=__UpperCamelCase	,				)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_train_samples )
												a__	:							Tuple      						= train_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="train dataset map pre-processing" ):
												a__	:							Optional[int]      						= train_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on train dataset"	,				)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(__UpperCamelCase ) )	,				3 ):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												a__	:							Optional[int]      						= min(len(__UpperCamelCase )	,				data_args.max_eval_samples )
												a__	:							Optional[Any]      						= eval_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="validation dataset map pre-processing" ):
												a__	:							List[Any]      						= eval_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on validation dataset"	,				)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_predict_samples )
												a__	:							List[str]      						= predict_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
												a__	:							Union[str, Any]      						= predict_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on prediction dataset"	,				)

    # Get the metric function
				a__	:							List[Any]      						= evaluate.load("xnli" )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(__UpperCamelCase ):
								a__	:							Dict      						= p.predictions[0] if isinstance(p.predictions	,				__UpperCamelCase ) else p.predictions
								a__	:							Any      						= np.argmax(__UpperCamelCase	,				axis=1 )
								return metric.compute(predictions=__UpperCamelCase	,				references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								a__	:							List[Any]      						= default_data_collator
				elif training_args.fpaa:
								a__	:							List[Any]      						= DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8 )
				else:
								a__	:							Tuple      						= None

				# Initialize our Trainer
				a__	:							List[str]      						= Trainer(
				    model=__UpperCamelCase	,				args=__UpperCamelCase	,				train_dataset=train_dataset if training_args.do_train else None	,				eval_dataset=eval_dataset if training_args.do_eval else None	,				compute_metrics=__UpperCamelCase	,				tokenizer=__UpperCamelCase	,				data_collator=__UpperCamelCase	,				)

				# Training
				if training_args.do_train:
								a__	:							str      						= None
								if training_args.resume_from_checkpoint is not None:
												a__	:							int      						= training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												a__	:							Optional[int]      						= last_checkpoint
								a__	:							Any      						= trainer.train(resume_from_checkpoint=__UpperCamelCase )
								a__	:							Tuple      						= train_result.metrics
								a__	:							Union[str, Any]      						= (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Optional[int]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"	,				__UpperCamelCase )
								trainer.save_metrics("train"	,				__UpperCamelCase )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***" )
								a__	:							Optional[int]      						= trainer.evaluate(eval_dataset=__UpperCamelCase )

								a__	:							Tuple      						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__UpperCamelCase )
								a__	:							List[Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("eval"	,				__UpperCamelCase )
								trainer.save_metrics("eval"	,				__UpperCamelCase )

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***" )
								a__						,   a__						,   a__	:							int      						= trainer.predict(__UpperCamelCase	,				metric_key_prefix="predict" )

								a__	:							int      						= (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Union[str, Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("predict"	,				__UpperCamelCase )
								trainer.save_metrics("predict"	,				__UpperCamelCase )

								a__	:							Dict      						= np.argmax(__UpperCamelCase	,				axis=1 )
								a__	:							str      						= os.path.join(training_args.output_dir	,				"predictions.txt" )
								if trainer.is_world_process_zero():
												with open(__UpperCamelCase	,				"w" ) as writer:
																writer.write("index\tprediction\n" )
																for index, item in enumerate(__UpperCamelCase ):
																				a__	:							int      						= label_list[item]
																				writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
						main()


