
'''simple docstring'''
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()
lowerCAmelCase					:    str           = logging.get_logger(__name__)







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







def 		A_(     A						:		Any):
							UpperCamelCase   			=     ['head.weight', 'head.bias']
							for k in ignore_keys:
														state_dict.pop(A		,     A)







def 		A_(     A						:		int		,     A						:		str		,     A						:		List[Any]):
							UpperCamelCase   			=     dct.pop(A)
							UpperCamelCase   			=     val







@torch.no_grad()
def 		A_(     A						:		Optional[int]		,     A						:		Optional[Any]):
							UpperCamelCase   			=     ViltConfig(image_size=384		,     patch_size=32		,     tie_word_embeddings=A)
							UpperCamelCase   			=     False
							UpperCamelCase   			=     False
							UpperCamelCase   			=     False
							UpperCamelCase   			=     False
							if "vqa" in checkpoint_url:
														UpperCamelCase   			=     True
														UpperCamelCase   			=     3129
														UpperCamelCase   			=     'huggingface/label-files'
														UpperCamelCase   			=     'vqa2-id2label.json'
														UpperCamelCase   			=     json.load(open(hf_hub_download(A		,     A		,     repo_type='dataset')		,     'r'))
														UpperCamelCase   			=     {int(A): v for k, v in idalabel.items()}
														UpperCamelCase   			=     idalabel
														UpperCamelCase   			=     {v: k for k, v in idalabel.items()}
														UpperCamelCase   			=     ViltForQuestionAnswering(A)
							elif "nlvr" in checkpoint_url:
														UpperCamelCase   			=     True
														UpperCamelCase   			=     2
														UpperCamelCase   			=     {0: 'False', 1: 'True'}
														UpperCamelCase   			=     {v: k for k, v in config.idalabel.items()}
														UpperCamelCase   			=     3
														UpperCamelCase   			=     ViltForImagesAndTextClassification(A)
							elif "irtr" in checkpoint_url:
														UpperCamelCase   			=     True
														UpperCamelCase   			=     ViltForImageAndTextRetrieval(A)
							elif "mlm_itm" in checkpoint_url:
														UpperCamelCase   			=     True
														UpperCamelCase   			=     ViltForMaskedLM(A)
							else:
														raise ValueError('Unknown model type')

							# load state_dict of original model, remove and rename some keys
							UpperCamelCase   			=     torch.hub.load_state_dict_from_url(A		,     map_location='cpu')['state_dict']
							UpperCamelCase   			=     create_rename_keys(A		,     A		,     A		,     A)
							for src, dest in rename_keys:
														rename_key(A		,     A		,     A)
							read_in_q_k_v(A		,     A)
							if mlm_model or irtr_model:
														UpperCamelCase   			=     ['itm_score.fc.weight', 'itm_score.fc.bias']
														for k in ignore_keys:
																					state_dict.pop(A		,     A)

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

							# Define processor
							UpperCamelCase   			=     ViltImageProcessor(size=384)
							UpperCamelCase   			=     BertTokenizer.from_pretrained('bert-base-uncased')
							UpperCamelCase   			=     ViltProcessor(A		,     A)

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

							# Verify outputs
							if mlm_model:
														UpperCamelCase   			=     torch.Size([1, 11, 3_0522])
														UpperCamelCase   			=     torch.tensor([-12.5_061, -12.5_123, -12.5_174])
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]		,     A		,     atol=1E-4)

														# verify masked token prediction equals "cats"
														UpperCamelCase   			=     outputs.logits[0, 4, :].argmax(-1).item()
														assert tokenizer.decode([predicted_id]) == "cats"
							elif vqa_model:
														UpperCamelCase   			=     torch.Size([1, 3129])
														UpperCamelCase   			=     torch.tensor([-15.9_495, -18.1_472, -10.3_041])
														assert torch.allclose(outputs.logits[0, :3]		,     A		,     atol=1E-4)
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]		,     A		,     atol=1E-4)

														# verify vqa prediction equals "2"
														UpperCamelCase   			=     outputs.logits.argmax(-1).item()
														assert model.config.idalabel[predicted_idx] == "2"
							elif nlvr_model:
														UpperCamelCase   			=     torch.Size([1, 2])
														UpperCamelCase   			=     torch.tensor([-2.8_721, 2.1_291])
														assert torch.allclose(outputs.logits[0, :3]		,     A		,     atol=1E-4)
														assert outputs.logits.shape == expected_shape

							Path(A).mkdir(exist_ok=A)
							print(f'''Saving model and processor to {pytorch_dump_folder_path}''')
							model.save_pretrained(A)
							processor.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    str           = 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.'
						)

						lowerCAmelCase					:    Tuple           = parser.parse_args()
						convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


if __name__ == "__main__":
						from doctest import testmod

						testmod()




