


from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class  _a							(    _lowercase):
	_a  :  Dict				   =							['''image_processor''', '''tokenizer''']
	_a  :  Dict				   =							'''Pix2StructImageProcessor'''
	_a  :  Any				   =							('''T5Tokenizer''', '''T5TokenizerFast''')




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		Dict		)->	Optional[Any]:
			lowerCAmelCase__       :      Optional[int]   					= False
			super().__init__(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def __call__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Any=None		, _SCREAMING_SNAKE_CASE						:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Union[bool, str, PaddingStrategy] = False		, _SCREAMING_SNAKE_CASE						:		Union[bool, str, TruncationStrategy] = None		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[int] = 2048		, _SCREAMING_SNAKE_CASE						:		int = 0		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		, _SCREAMING_SNAKE_CASE						:		Optional[bool] = None		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		bool = False		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		Optional[Union[str, TensorType]] = None		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	BatchEncoding:
			if images is None and text is None:
					raise ValueError('''You have to specify either images or text.'''		)

			# Get only text
			if images is None and not self.image_processor.is_vqa:
					lowerCAmelCase__       :      int   					= self.tokenizer
					lowerCAmelCase__       :      Any   					= self.tokenizer(
					    text=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_SNAKE_CASE		, stride=_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, return_overflowing_tokens=_SCREAMING_SNAKE_CASE		, return_special_tokens_mask=_SCREAMING_SNAKE_CASE		, return_offsets_mapping=_SCREAMING_SNAKE_CASE		, return_token_type_ids=_SCREAMING_SNAKE_CASE		, return_length=_SCREAMING_SNAKE_CASE		, verbose=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )
					return text_encoding

			if not self.image_processor.is_vqa:
					# add pixel_values
					lowerCAmelCase__       :      Any   					= self.image_processor(
					    _SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		, max_patches=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)
			else:
					# add pixel_values and bbox
					lowerCAmelCase__       :      Union[str, Any]   					= self.image_processor(
					    _SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		, max_patches=_SCREAMING_SNAKE_CASE		, header_text=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			if text is not None and not self.image_processor.is_vqa:
					lowerCAmelCase__       :      Union[str, Any]   					= self.tokenizer(
					    text=_SCREAMING_SNAKE_CASE		, add_special_tokens=_SCREAMING_SNAKE_CASE		, padding=_SCREAMING_SNAKE_CASE		, truncation=_SCREAMING_SNAKE_CASE		, max_length=_SCREAMING_SNAKE_CASE		, stride=_SCREAMING_SNAKE_CASE		, pad_to_multiple_of=_SCREAMING_SNAKE_CASE		, return_attention_mask=_SCREAMING_SNAKE_CASE		, return_overflowing_tokens=_SCREAMING_SNAKE_CASE		, return_special_tokens_mask=_SCREAMING_SNAKE_CASE		, return_offsets_mapping=_SCREAMING_SNAKE_CASE		, return_token_type_ids=_SCREAMING_SNAKE_CASE		, return_length=_SCREAMING_SNAKE_CASE		, verbose=_SCREAMING_SNAKE_CASE		, return_tensors=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

					if "attention_mask" in text_encoding:
							lowerCAmelCase__       :      Optional[Any]   					= text_encoding.pop('''attention_mask'''		)
					if "input_ids" in text_encoding:
							lowerCAmelCase__       :      Union[str, Any]   					= text_encoding.pop('''input_ids'''		)
			else:
					lowerCAmelCase__       :      int   					= None

			if text_encoding is not None:
					encoding_image_processor.update(_SCREAMING_SNAKE_CASE		)

			return encoding_image_processor




	def 							UpperCAmelCase__( self						:		List[str]		, *_SCREAMING_SNAKE_CASE						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	str:
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		List[Any]		, *_SCREAMING_SNAKE_CASE						:		Dict		, **_SCREAMING_SNAKE_CASE						:		List[Any]		)->	int:
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)




	@property
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	Optional[int]:
			lowerCAmelCase__       :      Tuple   					= self.tokenizer.model_input_names
			lowerCAmelCase__       :      int   					= self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)








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						=		logging.get_logger(__name__)






def 					lowerCamelCase_   (	_a     ,       _a=False	):




		"""simple docstring"""






		lowerCAmelCase__       :      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'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__       :      Tuple   					= [(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 					lowerCamelCase_   (	_a     ,       _a     ,       _a=False	):




		"""simple docstring"""






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






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= ['''head.weight''', '''head.bias''']
		for k in ignore_keys:
				state_dict.pop(_a     ,       _a	)






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Any   					= dct.pop(_a	)
		lowerCAmelCase__       :      Optional[Any]   					= val






def 					lowerCamelCase_   (	):




		"""simple docstring"""






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






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= ViTConfig()
		lowerCAmelCase__       :      Optional[Any]   					= False
		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		if vit_name[-5:] == "in21k":
				lowerCAmelCase__       :      List[Any]   					= True
				lowerCAmelCase__       :      Any   					= int(vit_name[-12:-10]	)
				lowerCAmelCase__       :      int   					= int(vit_name[-9:-6]	)
		else:
				lowerCAmelCase__       :      Dict   					= 1_000
				lowerCAmelCase__       :      str   					= '''huggingface/label-files'''
				lowerCAmelCase__       :      Dict   					= '''imagenet-1k-id2label.json'''
				lowerCAmelCase__       :      str   					= json.load(open(hf_hub_download(_a     ,       _a     ,       repo_type='''dataset'''	)     ,       '''r'''	)	)
				lowerCAmelCase__       :      Any   					= {int(_a	): v for k, v in idalabel.items()}
				lowerCAmelCase__       :      Optional[Any]   					= idalabel
				lowerCAmelCase__       :      List[str]   					= {v: k for k, v in idalabel.items()}
				lowerCAmelCase__       :      Tuple   					= int(vit_name[-6:-4]	)
				lowerCAmelCase__       :      Union[str, Any]   					= int(vit_name[-3:]	)
		# size of the architecture
		if "deit" in vit_name:
				if vit_name[9:].startswith('''tiny'''	):
						lowerCAmelCase__       :      List[str]   					= 192
						lowerCAmelCase__       :      Tuple   					= 768
						lowerCAmelCase__       :      Optional[int]   					= 12
						lowerCAmelCase__       :      List[Any]   					= 3
				elif vit_name[9:].startswith('''small'''	):
						lowerCAmelCase__       :      Any   					= 384
						lowerCAmelCase__       :      Optional[int]   					= 1_536
						lowerCAmelCase__       :      List[Any]   					= 12
						lowerCAmelCase__       :      Tuple   					= 6
				else:
						pass
		else:
				if vit_name[4:].startswith('''small'''	):
						lowerCAmelCase__       :      List[str]   					= 768
						lowerCAmelCase__       :      Tuple   					= 2_304
						lowerCAmelCase__       :      Any   					= 8
						lowerCAmelCase__       :      Union[str, Any]   					= 8
				elif vit_name[4:].startswith('''base'''	):
						pass
				elif vit_name[4:].startswith('''large'''	):
						lowerCAmelCase__       :      str   					= 1_024
						lowerCAmelCase__       :      Optional[Any]   					= 4_096
						lowerCAmelCase__       :      Optional[int]   					= 24
						lowerCAmelCase__       :      Tuple   					= 16
				elif vit_name[4:].startswith('''huge'''	):
						lowerCAmelCase__       :      Tuple   					= 1_280
						lowerCAmelCase__       :      Tuple   					= 5_120
						lowerCAmelCase__       :      Optional[int]   					= 32
						lowerCAmelCase__       :      List[Any]   					= 16

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

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

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

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

		if base_model:
				lowerCAmelCase__       :      Optional[Any]   					= 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:
				lowerCAmelCase__       :      Union[str, Any]   					= timm_model(_a	)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_a     ,       outputs.logits     ,       atol=1e-3	)

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


if __name__ == "__main__":
		lowerCamelCase						=		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						=		parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




