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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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






def 							__UpperCAmelCase     (							UpperCAmelCase,		UpperCAmelCase=False							)->							Any:



			"""simple docstring"""







			lowercase       		=		[]
			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')							)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ('''cls_token''', '''vit.embeddings.cls_token'''),
			        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
			        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
			        ('''pos_embed''', '''vit.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 "vit" from all keys that start with "vit"
						lowercase       		=		[(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'''),
						    ]							)

			return rename_keys






def 							__UpperCAmelCase     (							UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=False							)->							str:



			"""simple docstring"""







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






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



			"""simple docstring"""







			lowercase       		=		['''head.weight''', '''head.bias''']
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase_,		UpperCamelCase_							)






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



			"""simple docstring"""







			lowercase       		=		dct.pop(UpperCamelCase_							)
			lowercase       		=		val






def 							__UpperCAmelCase     (							)->							Tuple:



			"""simple docstring"""







			lowercase       		=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowercase       		=		Image.open(requests.get(UpperCamelCase_,		stream=UpperCamelCase_							).raw							)
			return im






@torch.no_grad()
def 							__UpperCAmelCase     (							UpperCAmelCase,		UpperCAmelCase							)->							Optional[Any]:



			"""simple docstring"""







			lowercase       		=		ViTConfig()
			lowercase       		=		False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						lowercase       		=		True
						lowercase       		=		int(vit_name[-12:-10]							)
						lowercase       		=		int(vit_name[-9:-6]							)
			else:
						lowercase       		=		1000
						lowercase       		=		'''huggingface/label-files'''
						lowercase       		=		'''imagenet-1k-id2label.json'''
						lowercase       		=		json.load(open(hf_hub_download(UpperCamelCase_,		UpperCamelCase_,		repo_type='''dataset'''							),		'''r'''							)							)
						lowercase       		=		{int(UpperCamelCase_							): v for k, v in idalabel.items()}
						lowercase       		=		idalabel
						lowercase       		=		{v: k for k, v in idalabel.items()}
						lowercase       		=		int(vit_name[-6:-4]							)
						lowercase       		=		int(vit_name[-3:]							)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith('''tiny'''							):
									lowercase       		=		192
									lowercase       		=		768
									lowercase       		=		12
									lowercase       		=		3
						elif vit_name[9:].startswith('''small'''							):
									lowercase       		=		384
									lowercase       		=		1536
									lowercase       		=		12
									lowercase       		=		6
						else:
									pass
			else:
						if vit_name[4:].startswith('''small'''							):
									lowercase       		=		768
									lowercase       		=		2304
									lowercase       		=		8
									lowercase       		=		8
						elif vit_name[4:].startswith('''base'''							):
									pass
						elif vit_name[4:].startswith('''large'''							):
									lowercase       		=		1024
									lowercase       		=		4096
									lowercase       		=		24
									lowercase       		=		16
						elif vit_name[4:].startswith('''huge'''							):
									lowercase       		=		1280
									lowercase       		=		5120
									lowercase       		=		32
									lowercase       		=		16

    # load original model from timm
			lowercase       		=		timm.create_model(UpperCamelCase_,		pretrained=UpperCamelCase_							)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			lowercase       		=		timm_model.state_dict()
			if base_model:
						remove_classification_head_(UpperCamelCase_							)
			lowercase       		=		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
			if vit_name[-5:] == "in21k":
						lowercase       		=		ViTModel(UpperCamelCase_							).eval()
			else:
						lowercase       		=		ViTForImageClassification(UpperCamelCase_							).eval()
			model.load_state_dict(UpperCamelCase_							)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						lowercase       		=		DeiTImageProcessor(size=config.image_size							)
			else:
						lowercase       		=		ViTImageProcessor(size=config.image_size							)
			lowercase       		=		image_processor(images=prepare_img(),		return_tensors='''pt'''							)
			lowercase       		=		encoding['''pixel_values''']
			lowercase       		=		model(UpperCamelCase_							)

			if base_model:
						lowercase       		=		timm_model.forward_features(UpperCamelCase_							)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(UpperCamelCase_,		outputs.pooler_output,		atol=1e-3							)
			else:
						lowercase       		=		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 {vit_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__":
						A_	     =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--vit_name",
						    default="vit_base_patch16_224",
						    type=str,
						    help="Name of the 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."
						)

						A_	     =   parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









'''simple docstring'''





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments



