





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   Tuple           =  DPTConfig(embedding_type='''hybrid'''			)

				if "large" in checkpoint_url:
								snake_case_						:   Optional[Any]           =  1_024
								snake_case_						:   Union[str, Any]           =  4_096
								snake_case_						:   str           =  24
								snake_case_						:   Optional[int]           =  16
								snake_case_						:   Optional[int]           =  [5, 11, 17, 23]
								snake_case_						:   Optional[int]           =  [256, 512, 1_024, 1_024]
								snake_case_						:   Any           =  (1, 384, 384)

				if "nyu" or "midas" in checkpoint_url:
								snake_case_						:   Optional[int]           =  768
								snake_case_						:   List[Any]           =  [1, 1, 1, 0.5]
								snake_case_						:   Union[str, Any]           =  [256, 512, 768, 768]
								snake_case_						:   Union[str, Any]           =  150
								snake_case_						:   List[Any]           =  16
								snake_case_						:   int           =  (1, 384, 384)
								snake_case_						:   Any           =  False
								snake_case_						:   Union[str, Any]           =  '''project'''

				if "ade" in checkpoint_url:
								snake_case_						:   Optional[int]           =  True
								snake_case_						:   List[Any]           =  768
								snake_case_						:   Dict           =  [1, 1, 1, 0.5]
								snake_case_						:   Any           =  150
								snake_case_						:   Dict           =  16
								snake_case_						:   int           =  '''huggingface/label-files'''
								snake_case_						:   List[str]           =  '''ade20k-id2label.json'''
								snake_case_						:   str           =  json.load(open(cached_download(hf_hub_url(_UpperCamelCase			,    _UpperCamelCase			,    repo_type='''dataset'''			)			)			,    '''r'''			)			)
								snake_case_						:   Any           =  {int(_UpperCamelCase			): v for k, v in idalabel.items()}
								snake_case_						:   Any           =  idalabel
								snake_case_						:   Any           =  {v: k for k, v in idalabel.items()}
								snake_case_						:   int           =  [1, 150, 480, 480]

				return config, expected_shape






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase			,    _UpperCamelCase			)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						str:






				"""simple docstring"""


				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								snake_case_						:   int           =  name.replace('''pretrained.model'''			,    '''dpt.encoder'''			)
				if "pretrained.model" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''pretrained.model'''			,    '''dpt.embeddings'''			)
				if "patch_embed" in name:
								snake_case_						:   str           =  name.replace('''patch_embed'''			,    ''''''			)
				if "pos_embed" in name:
								snake_case_						:   Tuple           =  name.replace('''pos_embed'''			,    '''position_embeddings'''			)
				if "attn.proj" in name:
								snake_case_						:   Dict           =  name.replace('''attn.proj'''			,    '''attention.output.dense'''			)
				if "proj" in name and "project" not in name:
								snake_case_						:   int           =  name.replace('''proj'''			,    '''projection'''			)
				if "blocks" in name:
								snake_case_						:   Tuple           =  name.replace('''blocks'''			,    '''layer'''			)
				if "mlp.fc1" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''mlp.fc1'''			,    '''intermediate.dense'''			)
				if "mlp.fc2" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''mlp.fc2'''			,    '''output.dense'''			)
				if "norm1" in name and "backbone" not in name:
								snake_case_						:   Tuple           =  name.replace('''norm1'''			,    '''layernorm_before'''			)
				if "norm2" in name and "backbone" not in name:
								snake_case_						:   Optional[Any]           =  name.replace('''norm2'''			,    '''layernorm_after'''			)
				if "scratch.output_conv" in name:
								snake_case_						:   List[Any]           =  name.replace('''scratch.output_conv'''			,    '''head'''			)
				if "scratch" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''scratch'''			,    '''neck'''			)
				if "layer1_rn" in name:
								snake_case_						:   int           =  name.replace('''layer1_rn'''			,    '''convs.0'''			)
				if "layer2_rn" in name:
								snake_case_						:   Any           =  name.replace('''layer2_rn'''			,    '''convs.1'''			)
				if "layer3_rn" in name:
								snake_case_						:   str           =  name.replace('''layer3_rn'''			,    '''convs.2'''			)
				if "layer4_rn" in name:
								snake_case_						:   Any           =  name.replace('''layer4_rn'''			,    '''convs.3'''			)
				if "refinenet" in name:
								snake_case_						:   Any           =  int(name[len('''neck.refinenet'''			) : len('''neck.refinenet'''			) + 1]			)
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								snake_case_						:   Union[str, Any]           =  name.replace(f'''refinenet{layer_idx}'''			,    f'''fusion_stage.layers.{abs(layer_idx-4			)}'''			)
				if "out_conv" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''out_conv'''			,    '''projection'''			)
				if "resConfUnit1" in name:
								snake_case_						:   Dict           =  name.replace('''resConfUnit1'''			,    '''residual_layer1'''			)
				if "resConfUnit2" in name:
								snake_case_						:   Optional[int]           =  name.replace('''resConfUnit2'''			,    '''residual_layer2'''			)
				if "conv1" in name:
								snake_case_						:   Any           =  name.replace('''conv1'''			,    '''convolution1'''			)
				if "conv2" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''conv2'''			,    '''convolution2'''			)
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''pretrained.act_postprocess1.0.project.0'''			,    '''neck.reassemble_stage.readout_projects.0.0'''			)
				if "pretrained.act_postprocess2.0.project.0" in name:
								snake_case_						:   Dict           =  name.replace('''pretrained.act_postprocess2.0.project.0'''			,    '''neck.reassemble_stage.readout_projects.1.0'''			)
				if "pretrained.act_postprocess3.0.project.0" in name:
								snake_case_						:   Any           =  name.replace('''pretrained.act_postprocess3.0.project.0'''			,    '''neck.reassemble_stage.readout_projects.2.0'''			)
				if "pretrained.act_postprocess4.0.project.0" in name:
								snake_case_						:   List[Any]           =  name.replace('''pretrained.act_postprocess4.0.project.0'''			,    '''neck.reassemble_stage.readout_projects.3.0'''			)

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								snake_case_						:   Union[str, Any]           =  name.replace('''pretrained.act_postprocess1.3'''			,    '''neck.reassemble_stage.layers.0.projection'''			)
				if "pretrained.act_postprocess1.4" in name:
								snake_case_						:   Optional[int]           =  name.replace('''pretrained.act_postprocess1.4'''			,    '''neck.reassemble_stage.layers.0.resize'''			)
				if "pretrained.act_postprocess2.3" in name:
								snake_case_						:   str           =  name.replace('''pretrained.act_postprocess2.3'''			,    '''neck.reassemble_stage.layers.1.projection'''			)
				if "pretrained.act_postprocess2.4" in name:
								snake_case_						:   str           =  name.replace('''pretrained.act_postprocess2.4'''			,    '''neck.reassemble_stage.layers.1.resize'''			)
				if "pretrained.act_postprocess3.3" in name:
								snake_case_						:   int           =  name.replace('''pretrained.act_postprocess3.3'''			,    '''neck.reassemble_stage.layers.2.projection'''			)
				if "pretrained.act_postprocess4.3" in name:
								snake_case_						:   List[Any]           =  name.replace('''pretrained.act_postprocess4.3'''			,    '''neck.reassemble_stage.layers.3.projection'''			)
				if "pretrained.act_postprocess4.4" in name:
								snake_case_						:   Any           =  name.replace('''pretrained.act_postprocess4.4'''			,    '''neck.reassemble_stage.layers.3.resize'''			)
				if "pretrained" in name:
								snake_case_						:   Dict           =  name.replace('''pretrained'''			,    '''dpt'''			)
				if "bn" in name:
								snake_case_						:   List[Any]           =  name.replace('''bn'''			,    '''batch_norm'''			)
				if "head" in name:
								snake_case_						:   List[Any]           =  name.replace('''head'''			,    '''head.head'''			)
				if "encoder.norm" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''encoder.norm'''			,    '''layernorm'''			)
				if "auxlayer" in name:
								snake_case_						:   int           =  name.replace('''auxlayer'''			,    '''auxiliary_head.head'''			)
				if "backbone" in name:
								snake_case_						:   Dict           =  name.replace('''backbone'''			,    '''backbone.bit.encoder'''			)

				if ".." in name:
								snake_case_						:   List[Any]           =  name.replace('''..'''			,    '''.'''			)

				if "stem.conv" in name:
								snake_case_						:   Optional[int]           =  name.replace('''stem.conv'''			,    '''bit.embedder.convolution'''			)
				if "blocks" in name:
								snake_case_						:   Tuple           =  name.replace('''blocks'''			,    '''layers'''			)
				if "convolution" in name and "backbone" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''convolution'''			,    '''conv'''			)
				if "layer" in name and "backbone" in name:
								snake_case_						:   Dict           =  name.replace('''layer'''			,    '''layers'''			)
				if "backbone.bit.encoder.bit" in name:
								snake_case_						:   Dict           =  name.replace('''backbone.bit.encoder.bit'''			,    '''backbone.bit'''			)
				if "embedder.conv" in name:
								snake_case_						:   Dict           =  name.replace('''embedder.conv'''			,    '''embedder.convolution'''			)
				if "backbone.bit.encoder.stem.norm" in name:
								snake_case_						:   Optional[Any]           =  name.replace('''backbone.bit.encoder.stem.norm'''			,    '''backbone.bit.embedder.norm'''			)
				return name






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				for i in range(config.num_hidden_layers			):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								snake_case_						:   int           =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''			)
								snake_case_						:   List[Any]           =  state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''			)
								# next, add query, keys and values (in that order) to the state dict
								snake_case_						:   int           =  in_proj_weight[: config.hidden_size, :]
								snake_case_						:   List[Any]           =  in_proj_bias[: config.hidden_size]
								snake_case_						:   List[Any]           =  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case_						:   Optional[Any]           =  in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								snake_case_						:   Optional[int]           =  in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case_						:   int           =  in_proj_bias[-config.hidden_size :]






def 					lowerCamelCase_						(    )		->						str:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:   List[str]           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)
				return im






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Tuple           =  get_dpt_config(_UpperCamelCase			)
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				snake_case_						:   Any           =  torch.load(_UpperCamelCase			,    map_location='''cpu'''			)
				# remove certain keys
				remove_ignore_keys_(_UpperCamelCase			)
				# rename keys
				for key in state_dict.copy().keys():
								snake_case_						:   Optional[Any]           =  state_dict.pop(_UpperCamelCase			)
								snake_case_						:   Dict           =  val
				# read in qkv matrices
				read_in_q_k_v(_UpperCamelCase			,    _UpperCamelCase			)

				# load HuggingFace model
				snake_case_						:   Any           =  DPTForSemanticSegmentation(_UpperCamelCase			) if '''ade''' in checkpoint_url else DPTForDepthEstimation(_UpperCamelCase			)
				model.load_state_dict(_UpperCamelCase			)
				model.eval()

				# Check outputs on an image
				snake_case_						:   Optional[Any]           =  480 if '''ade''' in checkpoint_url else 384
				snake_case_						:   Union[str, Any]           =  DPTImageProcessor(size=_UpperCamelCase			)

				snake_case_						:   Optional[Any]           =  prepare_img()
				snake_case_						:   Any           =  image_processor(_UpperCamelCase			,    return_tensors='''pt'''			)

				# forward pass
				snake_case_						:   Any           =  model(**_UpperCamelCase			).logits if '''ade''' in checkpoint_url else model(**_UpperCamelCase			).predicted_depth

				if show_prediction:
								snake_case_						:   List[Any]           =  (
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1			)			,    size=(image.size[1], image.size[0])			,    mode='''bicubic'''			,    align_corners=_UpperCamelCase			,    )
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 255			).show()

				if pytorch_dump_folder_path is not None:
								Path(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
								print(f'''Saving model 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 push_to_hub:
								model.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)
								image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''			)


if __name__ == "__main__":
	lowerCAmelCase_    =    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--checkpoint_url''',
	    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
	    type=str,
	    help='''URL of the original DPT checkpoint you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''',
	    default=None,
	    type=str,
	    required=False,
	    help='''Path to the output PyTorch model directory.''',
	)
	parser.add_argument(
	    '''--push_to_hub''',
	    action='''store_true''',
	)
	parser.add_argument(
	    '''--model_name''',
	    default='''dpt-large''',
	    type=str,
	    help='''Name of the model, in case you\'re pushing to the hub.''',
	)
	parser.add_argument(
	    '''--show_prediction''',
	    action='''store_true''',
	)

	lowerCAmelCase_    =    parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)







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  __lowerCAmelCase	:
			lowerCamelCase_					:					Optional[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.'''
			        )
			    },					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
			lowerCamelCase_					:					bool	      =				field(
			    default=_a,					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.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
			            '''value if set.'''
			        )
			    },					)
			lowerCamelCase_					:					Optional[int]	      =				field(
			    default=_a,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
			            '''value if set.'''
			        )
			    },					)




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






def 					lowerCamelCase_						(    )		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				snake_case_      ,							snake_case_      ,							snake_case_						:   Tuple           =  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()

				snake_case_						:   List[Any]           =  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.
				snake_case_						:   str           =  None
				if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
								snake_case_						:   Optional[Any]           =  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:
												snake_case_						:   Union[str, 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:
												snake_case_						:   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			,    )
								snake_case_						:   Optional[int]           =  train_dataset.features['''label'''].names

				if training_args.do_eval:
								snake_case_						:   Dict           =  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			,    )
								snake_case_						:   Tuple           =  eval_dataset.features['''label'''].names

				if training_args.do_predict:
								snake_case_						:   int           =  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			,    )
								snake_case_						:   Optional[int]           =  predict_dataset.features['''label'''].names

				# Labels
				snake_case_						:   int           =  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.
				snake_case_						:   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			,    )
				snake_case_						:   Dict           =  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			,    )
				snake_case_						:   Union[str, Any]           =  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:
								snake_case_						:   Dict           =  '''max_length'''
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								snake_case_						:   str           =  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:
												snake_case_						:   List[Any]           =  min(len(_UpperCamelCase			)			,    data_args.max_train_samples			)
												snake_case_						:   int           =  train_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''train dataset map pre-processing'''			):
												snake_case_						:   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:
												snake_case_						:   List[str]           =  min(len(_UpperCamelCase			)			,    data_args.max_eval_samples			)
												snake_case_						:   List[str]           =  eval_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''validation dataset map pre-processing'''			):
												snake_case_						:   List[str]           =  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:
												snake_case_						:   Union[str, Any]           =  min(len(_UpperCamelCase			)			,    data_args.max_predict_samples			)
												snake_case_						:   Dict           =  predict_dataset.select(range(_UpperCamelCase			)			)
								with training_args.main_process_first(desc='''prediction dataset map pre-processing'''			):
												snake_case_						:   List[str]           =  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
				snake_case_						:   int           =  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			):
								snake_case_						:   List[str]           =  p.predictions[0] if isinstance(p.predictions			,    _UpperCamelCase			) else p.predictions
								snake_case_						:   Tuple           =  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:
								snake_case_						:   Optional[int]           =  default_data_collator
				elif training_args.fpaa:
								snake_case_						:   Any           =  DataCollatorWithPadding(_UpperCamelCase			,    pad_to_multiple_of=8			)
				else:
								snake_case_						:   Any           =  None

				# Initialize our Trainer
				snake_case_						:   Any           =  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:
								snake_case_						:   int           =  None
								if training_args.resume_from_checkpoint is not None:
												snake_case_						:   Dict           =  training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												snake_case_						:   Dict           =  last_checkpoint
								snake_case_						:   int           =  trainer.train(resume_from_checkpoint=_UpperCamelCase			)
								snake_case_						:   Union[str, Any]           =  train_result.metrics
								snake_case_						:   Union[str, Any]           =  (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(_UpperCamelCase			)
								)
								snake_case_						:   Dict           =  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 ***'''			)
								snake_case_						:   Any           =  trainer.evaluate(eval_dataset=_UpperCamelCase			)

								snake_case_						:   int           =  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_UpperCamelCase			)
								snake_case_						:   str           =  min(_UpperCamelCase			,    len(_UpperCamelCase			)			)

								trainer.log_metrics('''eval'''			,    _UpperCamelCase			)
								trainer.save_metrics('''eval'''			,    _UpperCamelCase			)

				# Prediction
				if training_args.do_predict:
								logger.info('''*** Predict ***'''			)
								snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  trainer.predict(_UpperCamelCase			,    metric_key_prefix='''predict'''			)

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

								trainer.log_metrics('''predict'''			,    _UpperCamelCase			)
								trainer.save_metrics('''predict'''			,    _UpperCamelCase			)

								snake_case_						:   List[Any]           =  np.argmax(_UpperCamelCase			,    axis=1			)
								snake_case_						:   Optional[Any]           =  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			):
																				snake_case_						:   List[str]           =  label_list[item]
																				writer.write(f'''{index}\t{item}\n'''			)


if __name__ == "__main__":
	main()
