



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()
_lowerCAmelCase	:   int    =   logging.get_logger(__name__)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__=False					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				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"
								lowerCAmelCase__		  =  [(pair[0], pair[1][4:]) if pair[1].startswith('vit'					) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('norm.weight', 'vit.layernorm.weight'),
								        ('norm.bias', 'vit.layernorm.bias'),
								        ('head.weight', 'classifier.weight'),
								        ('head.bias', 'classifier.bias'),
								    ]					)

				return rename_keys







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=False					)	->     str:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  dct.pop(snake_case__					)
				lowerCAmelCase__		  =  val







def     UpperCAmelCase_		(					)	->     str:



				"""simple docstring"""





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







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     List[Any]:



				"""simple docstring"""





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

    # load original model from timm
				lowerCAmelCase__		  =  timm.create_model(snake_case__    ,			pretrained=snake_case__					)
				timm_model.eval()

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

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowerCAmelCase__		  =  ViTModel(snake_case__					).eval()
				else:
								lowerCAmelCase__		  =  ViTForImageClassification(snake_case__					).eval()
				model.load_state_dict(snake_case__					)

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

				if base_model:
								lowerCAmelCase__		  =  timm_model.forward_features(snake_case__					)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(snake_case__    ,			outputs.pooler_output    ,			atol=1E-3					)
				else:
								lowerCAmelCase__		  =  timm_model(snake_case__					)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(snake_case__    ,			outputs.logits    ,			atol=1E-3					)

				Path(snake_case__					).mkdir(exist_ok=snake_case__					)
				print(f'Saving model {vit_name} to {pytorch_dump_folder_path}'					)
				model.save_pretrained(snake_case__					)
				print(f'Saving image processor to {pytorch_dump_folder_path}'					)
				image_processor.save_pretrained(snake_case__					)


if __name__ == "__main__":
	_lowerCAmelCase	:   List[str]    =   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."
	)

	_lowerCAmelCase	:   Any    =   parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_=""    ,a_="train"						):







						"""simple docstring"""
						assert os.path.isdir(a_						)

						lowerCAmelCase__		  =  []
						lowerCAmelCase__		  =  os.listdir(a_						)
						for story_filename in story_filenames_list:
										if "summary" in story_filename:
														continue
										lowerCAmelCase__		  =  os.path.join(a_    ,a_						)
										if not os.path.isfile(a_						):
														continue
										self.documents.append(a_						)
		def __len__(							self						):







						"""simple docstring"""
						return len(self.documents						)



		def __getitem__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.documents[idx]
						lowerCAmelCase__		  =  document_path.split('/'						)[-1]
						with open(a_    ,encoding='utf-8'						) as source:
										lowerCAmelCase__		  =  source.read()
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  process_story(a_						)
						return document_name, story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: len(snake_case__					) != 0    ,			[line.strip() for line in raw_story.split('\n'					)]					)					)

				# for some unknown reason some lines miss a period, add it
				lowerCAmelCase__		  =  [_add_missing_period(snake_case__					) for line in nonempty_lines]

				# gather article lines
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  deque(snake_case__					)
				while True:
								try:
												lowerCAmelCase__		  =  lines.popleft()
												if element.startswith('@highlight'					):
																break
												story_lines.append(snake_case__					)
								except IndexError:
												# if "@highlight" is absent from the file we pop
												# all elements until there is None, raising an exception.
												return story_lines, []

    # gather summary lines
				lowerCAmelCase__		  =  list(filter(lambda snake_case__					: not t.startswith('@highlight'					)    ,			snake_case__					)					)

				return story_lines, summary_lines







def     UpperCAmelCase_		(					snake_case__					)	->     Optional[Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
				if line.startswith('@highlight'					):
								return line
				if line[-1] in END_TOKENS:
								return line
				return line + "."







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				if len(snake_case__					) > block_size:
								return sequence[:block_size]
				else:
								sequence.extend([pad_token_id] * (block_size - len(snake_case__					))					)
								return sequence







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.ones_like(snake_case__					)
				lowerCAmelCase__		  =  sequence == pad_token_id
				lowerCAmelCase__		  =  0
				return mask







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in story_lines]
				lowerCAmelCase__		  =  [token for sentence in story_lines_token_ids for token in sentence]
				lowerCAmelCase__		  =  [tokenizer.encode(snake_case__					) for line in summary_lines]
				lowerCAmelCase__		  =  [token for sentence in summary_lines_token_ids for token in sentence]

				return story_token_ids, summary_token_ids







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



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				for sequence in batch:
								lowerCAmelCase__		  =  -1
								lowerCAmelCase__		  =  []
								for s in sequence:
												if s == separator_token_id:
																sentence_num += 1
												embeddings.append(sentence_num % 2					)
								batch_embeddings.append(snake_case__					)
				return torch.tensor(snake_case__					)
