
"""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()
_lowercase							      = logging.get_logger(__name__)






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






def 					_snake_case (						snake_case__      :   Optional[int]   ):
	A        =      ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(snake_case__		,					snake_case__   )






def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   Optional[Any]		,					snake_case__      :   List[str]   ):
	A        =      dct.pop(snake_case__   )
	A        =      val






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

	# load state_dict of original model, remove and rename some keys
	A        =      torch.hub.load_state_dict_from_url(snake_case__		,					map_location='cpu'   )['state_dict']
	A        =      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:
		A        =      ['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:
		A      ,					A        =      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
	A        =      ViltImageProcessor(size=384   )
	A        =      BertTokenizer.from_pretrained('bert-base-uncased'   )
	A        =      ViltProcessor(snake_case__		,					snake_case__   )

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

	# Verify outputs
	if mlm_model:
		A        =      torch.Size([1, 11, 3_0522]   )
		A        =      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"
		A        =      outputs.logits[0, 4, :].argmax(-1   ).item()
		assert tokenizer.decode([predicted_id]   ) == "cats"
	elif vqa_model:
		A        =      torch.Size([1, 3129]   )
		A        =      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"
		A        =      outputs.logits.argmax(-1   ).item()
		assert model.config.idalabel[predicted_idx] == "2"
	elif nlvr_model:
		A        =      torch.Size([1, 2]   )
		A        =      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__":
		_lowercase							      = 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.'''
		)

		_lowercase							      = parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
"""simple docstring"""






import math



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * power_factor



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				if (
				    not isinstance(lowerCamelCase					,			(int, float)       )
				    or power_factor < -1
				    or power_factor > 1
				):
								raise ValueError('power_factor must be a valid float value between -1 and 1.'       )
				return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()



