






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


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




def      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							Union[str, Any]=False     ,       __snake_case    :							int=False     ,       __snake_case    :							int=False  ):


					'''simple docstring'''


					UpperCAmelCase_    :      int            =						[]
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"transformer.blocks.{i}.norm1.weight", F"vilt.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"transformer.blocks.{i}.norm1.bias", F"vilt.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append(
										    (F"transformer.blocks.{i}.attn.proj.weight", F"vilt.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append(
										    (F"transformer.blocks.{i}.attn.proj.bias", F"vilt.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"transformer.blocks.{i}.norm2.weight", F"vilt.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"transformer.blocks.{i}.norm2.bias", F"vilt.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append(
										    (F"transformer.blocks.{i}.mlp.fc1.weight", F"vilt.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"transformer.blocks.{i}.mlp.fc1.bias", F"vilt.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.weight", F"vilt.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"transformer.blocks.{i}.mlp.fc2.bias", F"vilt.encoder.layer.{i}.output.dense.bias")  )

					# embeddings
					rename_keys.extend(
					    [
					        # text embeddings
					        ('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
					        (
					            'text_embeddings.position_embeddings.weight',
					            'vilt.embeddings.text_embeddings.position_embeddings.weight',
					        ),
					        ('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
					        (
					            'text_embeddings.token_type_embeddings.weight',
					            'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
					        ),
					        ('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
					        ('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
					        # patch embeddings
					        ('transformer.cls_token', 'vilt.embeddings.cls_token'),
					        ('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
					        ('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
					        ('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
					        # token type embeddings
					        ('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
					    ]  )

					# final layernorm + pooler
					rename_keys.extend(
					    [
					        ('transformer.norm.weight', 'vilt.layernorm.weight'),
					        ('transformer.norm.bias', 'vilt.layernorm.bias'),
					        ('pooler.dense.weight', 'vilt.pooler.dense.weight'),
					        ('pooler.dense.bias', 'vilt.pooler.dense.bias'),
					    ]  )

					# classifier head(s)
					if vqa_model:
										# classification head
										rename_keys.extend(
										    [
										        ('vqa_classifier.0.weight', 'classifier.0.weight'),
										        ('vqa_classifier.0.bias', 'classifier.0.bias'),
										        ('vqa_classifier.1.weight', 'classifier.1.weight'),
										        ('vqa_classifier.1.bias', 'classifier.1.bias'),
										        ('vqa_classifier.3.weight', 'classifier.3.weight'),
										        ('vqa_classifier.3.bias', 'classifier.3.bias'),
										    ]  )
					elif nlvr_model:
										# classification head
										rename_keys.extend(
										    [
										        ('nlvr2_classifier.0.weight', 'classifier.0.weight'),
										        ('nlvr2_classifier.0.bias', 'classifier.0.bias'),
										        ('nlvr2_classifier.1.weight', 'classifier.1.weight'),
										        ('nlvr2_classifier.1.bias', 'classifier.1.bias'),
										        ('nlvr2_classifier.3.weight', 'classifier.3.weight'),
										        ('nlvr2_classifier.3.bias', 'classifier.3.bias'),
										    ]  )
					else:
										pass

					return rename_keys




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							Tuple  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[Any]            =						['head.weight', 'head.bias']
					for k in ignore_keys:
										state_dict.pop(_snake_case     ,       _snake_case  )




def      lowercase__      (     __snake_case    :							Union[str, Any]     ,       __snake_case    :							Dict     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      List[str]            =						dct.pop(_snake_case  )
					UpperCAmelCase_    :      int            =						val




@torch.no_grad()
def      lowercase__      (     __snake_case    :							str     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						ViltConfig(image_size=384     ,       patch_size=32     ,       tie_word_embeddings=_snake_case  )
					UpperCAmelCase_    :      List[Any]            =						False
					UpperCAmelCase_    :      Optional[Any]            =						False
					UpperCAmelCase_    :      Any            =						False
					UpperCAmelCase_    :      List[Any]            =						False
					if "vqa" in checkpoint_url:
										UpperCAmelCase_    :      Any            =						True
										UpperCAmelCase_    :      Tuple            =						3_129
										UpperCAmelCase_    :      Dict            =						'huggingface/label-files'
										UpperCAmelCase_    :      Tuple            =						'vqa2-id2label.json'
										UpperCAmelCase_    :      List[Any]            =						json.load(open(hf_hub_download(_snake_case     ,       _snake_case     ,       repo_type='dataset'  )     ,       'r'  )  )
										UpperCAmelCase_    :      Optional[int]            =						{int(_snake_case  ): v for k, v in idalabel.items()}
										UpperCAmelCase_    :      Optional[int]            =						idalabel
										UpperCAmelCase_    :      Any            =						{v: k for k, v in idalabel.items()}
										UpperCAmelCase_    :      Optional[int]            =						ViltForQuestionAnswering(_snake_case  )
					elif "nlvr" in checkpoint_url:
										UpperCAmelCase_    :      List[str]            =						True
										UpperCAmelCase_    :      List[str]            =						2
										UpperCAmelCase_    :      Union[str, Any]            =						{0: 'False', 1: 'True'}
										UpperCAmelCase_    :      List[str]            =						{v: k for k, v in config.idalabel.items()}
										UpperCAmelCase_    :      str            =						3
										UpperCAmelCase_    :      Dict            =						ViltForImagesAndTextClassification(_snake_case  )
					elif "irtr" in checkpoint_url:
										UpperCAmelCase_    :      Any            =						True
										UpperCAmelCase_    :      Optional[int]            =						ViltForImageAndTextRetrieval(_snake_case  )
					elif "mlm_itm" in checkpoint_url:
										UpperCAmelCase_    :      Dict            =						True
										UpperCAmelCase_    :      str            =						ViltForMaskedLM(_snake_case  )
					else:
										raise ValueError('Unknown model type'  )

					# load state_dict of original model, remove and rename some keys
					UpperCAmelCase_    :      Any            =						torch.hub.load_state_dict_from_url(_snake_case     ,       map_location='cpu'  )['state_dict']
					UpperCAmelCase_    :      Tuple            =						create_rename_keys(_snake_case     ,       _snake_case     ,       _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  )
					if mlm_model or irtr_model:
										UpperCAmelCase_    :      Optional[Any]            =						['itm_score.fc.weight', 'itm_score.fc.bias']
										for k in ignore_keys:
															state_dict.pop(_snake_case     ,       _snake_case  )

    # load state dict into HuggingFace model
					model.eval()
					if mlm_model:
										UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						model.load_state_dict(_snake_case     ,       strict=_snake_case  )
										assert missing_keys == ["mlm_score.decoder.bias"]
					else:
										model.load_state_dict(_snake_case  )

					# Define processor
					UpperCAmelCase_    :      int            =						ViltImageProcessor(size=384  )
					UpperCAmelCase_    :      Dict            =						BertTokenizer.from_pretrained('bert-base-uncased'  )
					UpperCAmelCase_    :      Any            =						ViltProcessor(_snake_case     ,       _snake_case  )

					# Forward pass on example inputs (image + text)
					if nlvr_model:
										UpperCAmelCase_    :      Tuple            =						Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,       stream=_snake_case  ).raw  )
										UpperCAmelCase_    :      Optional[Any]            =						Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'     ,       stream=_snake_case  ).raw  )
										UpperCAmelCase_    :      Dict            =						(
										    'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
										    ' standing.'
										)
										UpperCAmelCase_    :      Any            =						processor(_snake_case     ,       _snake_case     ,       return_tensors='pt'  )
										UpperCAmelCase_    :      Union[str, Any]            =						processor(_snake_case     ,       _snake_case     ,       return_tensors='pt'  )
										UpperCAmelCase_    :      Tuple            =						model(
										    input_ids=encoding_a.input_ids     ,       pixel_values=encoding_a.pixel_values     ,       pixel_values_a=encoding_a.pixel_values     ,       )
					else:
										UpperCAmelCase_    :      Union[str, Any]            =						Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'     ,       stream=_snake_case  ).raw  )
										if mlm_model:
															UpperCAmelCase_    :      str            =						'a bunch of [MASK] laying on a [MASK].'
										else:
															UpperCAmelCase_    :      Union[str, Any]            =						'How many cats are there?'
										UpperCAmelCase_    :      Tuple            =						processor(_snake_case     ,       _snake_case     ,       return_tensors='pt'  )
										UpperCAmelCase_    :      str            =						model(**_snake_case  )

					# Verify outputs
					if mlm_model:
										UpperCAmelCase_    :      str            =						torch.Size([1, 11, 30_522]  )
										UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([-12.5061, -12.5123, -12.5174]  )
										assert outputs.logits.shape == expected_shape
										assert torch.allclose(outputs.logits[0, 0, :3]     ,       _snake_case     ,       atol=1E-4  )

										# verify masked token prediction equals "cats"
										UpperCAmelCase_    :      Tuple            =						outputs.logits[0, 4, :].argmax(-1  ).item()
										assert tokenizer.decode([predicted_id]  ) == "cats"
					elif vqa_model:
										UpperCAmelCase_    :      int            =						torch.Size([1, 3_129]  )
										UpperCAmelCase_    :      List[str]            =						torch.tensor([-15.9495, -18.1472, -10.3041]  )
										assert torch.allclose(outputs.logits[0, :3]     ,       _snake_case     ,       atol=1E-4  )
										assert outputs.logits.shape == expected_shape
										assert torch.allclose(outputs.logits[0, 0, :3]     ,       _snake_case     ,       atol=1E-4  )

										# verify vqa prediction equals "2"
										UpperCAmelCase_    :      List[Any]            =						outputs.logits.argmax(-1  ).item()
										assert model.config.idalabel[predicted_idx] == "2"
					elif nlvr_model:
										UpperCAmelCase_    :      Tuple            =						torch.Size([1, 2]  )
										UpperCAmelCase_    :      List[str]            =						torch.tensor([-2.8721, 2.1291]  )
										assert torch.allclose(outputs.logits[0, :3]     ,       _snake_case     ,       atol=1E-4  )
										assert outputs.logits.shape == expected_shape

					Path(_snake_case  ).mkdir(exist_ok=_snake_case  )
					print(F"Saving model and processor to {pytorch_dump_folder_path}"  )
					model.save_pretrained(_snake_case  )
					processor.save_pretrained(_snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase								=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--checkpoint_url',
		    default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
		    type=str,
		    help='URL of the checkpoint you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)

		__UpperCAmelCase								=						parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			Union[str, Any]  				=			{"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'ctrl'
			SCREAMING_SNAKE_CASE							   =						['past_key_values']
			SCREAMING_SNAKE_CASE							   =						{
			    'max_position_embeddings': 'n_positions',
			    'hidden_size': 'n_embd',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}





			def __init__(		self					,  __snake_case=24_6534					,  __snake_case=256					,  __snake_case=1280					,  __snake_case=8192					,  __snake_case=48					,  __snake_case=16					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=1e-6					,  __snake_case=0.02					,  __snake_case=True					,  **__snake_case					,  )   -> Union[str, Any]:




						'''simple docstring'''
						__a        =vocab_size
						__a        =n_positions
						__a        =n_embd
						__a        =n_layer
						__a        =n_head
						__a        =dff
						__a        =resid_pdrop
						__a        =embd_pdrop
						__a        =layer_norm_epsilon
						__a        =initializer_range

						__a        =use_cache

						super().__init__(**__snake_case					)






