
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					: Any      =       logging.get_logger(__name__)


def       A       (					lowercase    ,       lowercase=False    ,       lowercase=False    ,       lowercase=False )   ->  Optional[int]:
		'''simple docstring'''

		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       (					lowercase    ,       lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		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       (					lowercase )   ->  Tuple:
		'''simple docstring'''

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


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     dct.pop(lowercase )
		UpperCamelCase		=     val


@torch.no_grad()
def       A       (					lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     ViltConfig(image_size=384    ,       patch_size=32    ,       tie_word_embeddings=lowercase )
		UpperCamelCase		=     False
		UpperCamelCase		=     False
		UpperCamelCase		=     False
		UpperCamelCase		=     False
		if "vqa" in checkpoint_url:
				UpperCamelCase		=     True
				UpperCamelCase		=     3_129
				UpperCamelCase		=     'huggingface/label-files'
				UpperCamelCase		=     'vqa2-id2label.json'
				UpperCamelCase		=     json.load(open(hf_hub_download(lowercase    ,       lowercase    ,       repo_type='dataset' )    ,       'r' ) )
				UpperCamelCase		=     {int(lowercase ): v for k, v in idalabel.items()}
				UpperCamelCase		=     idalabel
				UpperCamelCase		=     {v: k for k, v in idalabel.items()}
				UpperCamelCase		=     ViltForQuestionAnswering(lowercase )
		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(lowercase )
		elif "irtr" in checkpoint_url:
				UpperCamelCase		=     True
				UpperCamelCase		=     ViltForImageAndTextRetrieval(lowercase )
		elif "mlm_itm" in checkpoint_url:
				UpperCamelCase		=     True
				UpperCamelCase		=     ViltForMaskedLM(lowercase )
		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(lowercase    ,       map_location='cpu' )['state_dict']
		UpperCamelCase		=     create_rename_keys(lowercase    ,       lowercase    ,       lowercase    ,       lowercase )
		for src, dest in rename_keys:
				rename_key(lowercase    ,       lowercase    ,       lowercase )
		read_in_q_k_v(lowercase    ,       lowercase )
		if mlm_model or irtr_model:
				UpperCamelCase		=     ['itm_score.fc.weight', 'itm_score.fc.bias']
				for k in ignore_keys:
						state_dict.pop(lowercase    ,       lowercase )

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

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

		# 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=lowercase ).raw )
				UpperCamelCase		=     Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'    ,       stream=lowercase ).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(lowercase    ,       lowercase    ,       return_tensors='pt' )
				UpperCamelCase		=     processor(lowercase    ,       lowercase    ,       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=lowercase ).raw )
				if mlm_model:
						UpperCamelCase		=     'a bunch of [MASK] laying on a [MASK].'
				else:
						UpperCamelCase		=     'How many cats are there?'
				UpperCamelCase		=     processor(lowercase    ,       lowercase    ,       return_tensors='pt' )
				UpperCamelCase		=     model(**lowercase )

		# Verify outputs
		if mlm_model:
				UpperCamelCase		=     torch.Size([1, 11, 30_522] )
				UpperCamelCase		=     torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4] )
				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, 0, :3]    ,       lowercase    ,       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, 3_129] )
				UpperCamelCase		=     torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1] )
				assert torch.allclose(outputs.logits[0, :3]    ,       lowercase    ,       atol=1e-4 )
				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, 0, :3]    ,       lowercase    ,       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_7_2_1, 2.1_2_9_1] )
				assert torch.allclose(outputs.logits[0, :3]    ,       lowercase    ,       atol=1e-4 )
				assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
					_UpperCAmelCase					: List[Any]      =       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					: Tuple      =       parser.parse_args()
					convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


import requests


def       A       (					lowercase    ,       lowercase )   ->  None:
		'''simple docstring'''

		UpperCamelCase		=     {'Content-Type': 'application/json'}
		UpperCamelCase		=     requests.post(lowercase    ,       json={'text': message_body}    ,       headers=lowercase )
		if response.status_code != 200:
				UpperCamelCase		=     (
				    'Request to slack returned an error '
				    f'''{response.status_code}, the response is:\n{response.text}'''
				)
				raise ValueError(lowercase )


if __name__ == "__main__":
					# Set the slack url to the one provided by Slack when you create the webhook at
					# https://my.slack.com/services/new/incoming-webhook/
					send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
