
"""simple docstring"""




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()
lowerCAmelCase__							=				logging.get_logger(__name__)



def        _lowerCamelCase ( __a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = []

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



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(__a,							__a    )



def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = dct.pop(__a    )
		SCREAMING_SNAKE_CASE_						 = val



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		return im



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a=False    ):
		SCREAMING_SNAKE_CASE_						 = BitConfig(
		    global_padding='''same''',							layer_type='''bottleneck''',							depths=(3, 4, 9),							out_features=['''stage3'''],							embedding_dynamic_padding=__a,							)
		SCREAMING_SNAKE_CASE_						 = ViTHybridConfig(backbone_config=__a,							image_size=384,							num_labels=1_000    )
		SCREAMING_SNAKE_CASE_						 = False

		# load original model from timm
		SCREAMING_SNAKE_CASE_						 = timm.create_model(__a,							pretrained=__a    )
		timm_model.eval()

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

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

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

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

		SCREAMING_SNAKE_CASE_						 = {
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

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

		SCREAMING_SNAKE_CASE_						 = prepare_img()
		SCREAMING_SNAKE_CASE_						 = transform(__a    ).unsqueeze(0    )
		SCREAMING_SNAKE_CASE_						 = processor(__a,							return_tensors='''pt'''    ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_						 = model(__a    )
				SCREAMING_SNAKE_CASE_						 = outputs.logits

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

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

		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__":
						lowerCAmelCase__							=				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.'
						)

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

"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
		return (preds == labels).mean()





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







@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
							UpperCAmelCase__						     =      field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
							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=__lowercase       ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







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.

		SCREAMING_SNAKE_CASE_						 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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 if training_args.local_rank in [-1, 0] else logging.WARN,							)
		logger.warning(
		    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',							training_args.local_rank,							training_args.device,							training_args.n_gpu,							bool(training_args.local_rank != -1    ),							training_args.fpaa,							)
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank    ):
				transformers.utils.logging.set_verbosity_info()
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()
		logger.info('''Training/evaluation parameters %s''',							__a    )

		# Set seed
		set_seed(training_args.seed    )

		try:
				SCREAMING_SNAKE_CASE_						 = processors[data_args.task_name]()
				SCREAMING_SNAKE_CASE_						 = processor.get_labels()
				SCREAMING_SNAKE_CASE_						 = len(__a    )
		except KeyError:
				raise ValueError('''Task not found: %s''' % (data_args.task_name)    )

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path,							num_labels=__a,							finetuning_task=data_args.task_name,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = 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,							)
		SCREAMING_SNAKE_CASE_						 = AutoModelForMultipleChoice.from_pretrained(
		    model_args.model_name_or_path,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path    ),							config=__a,							cache_dir=model_args.cache_dir,							)

		# Get datasets
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.train,							)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.dev,							)
		    if training_args.do_eval
		    else None
		)

		def compute_metrics(__a    ) -> Dict:
				SCREAMING_SNAKE_CASE_						 = np.argmax(p.predictions,							axis=1    )
				return {"acc": simple_accuracy(__a,							p.label_ids    )}

		# Data collator
		SCREAMING_SNAKE_CASE_						 = DataCollatorWithPadding(__a,							pad_to_multiple_of=8    ) if training_args.fpaa else None

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE_						 = Trainer(
		    model=__a,							args=__a,							train_dataset=__a,							eval_dataset=__a,							compute_metrics=__a,							data_collator=__a,							)

		# Training
		if training_args.do_train:
				trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
				trainer.save_model()
				# For convenience, we also re-save the tokenizer to the same directory,
				# so that you can share your model easily on huggingface.co/models =)
				if trainer.is_world_master():
						tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
		SCREAMING_SNAKE_CASE_						 = {}
		if training_args.do_eval:
				logger.info('''*** Evaluate ***'''    )

				SCREAMING_SNAKE_CASE_						 = trainer.evaluate()

				SCREAMING_SNAKE_CASE_						 = os.path.join(training_args.output_dir,							'''eval_results.txt'''    )
				if trainer.is_world_master():
						with open(__a,							'''w'''    ) as writer:
								logger.info('''***** Eval results *****'''    )
								for key, value in result.items():
										logger.info('''  %s = %s''',							__a,							__a    )
										writer.write('''%s = %s\n''' % (key, value)    )

								results.update(__a    )

		return results



def        _lowerCamelCase ( __a    ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()