






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					:		Optional[Any]	  =						logging.get_logger(__name__)
def      a_ (      _A      ,   _A=False      ,   _A=False      ,   _A=False  )			->			Any:







				"""simple docstring"""
				snake_case__									=     []
				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      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""






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







				"""simple docstring"""
				snake_case__									=     ["head.weight", "head.bias"]
				for k in ignore_keys:
								state_dict.pop(__SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE  )
def      a_ (      _A      ,   _A      ,   _A  )			->			List[str]:







				"""simple docstring"""
				snake_case__									=     dct.pop(__SCREAMING_SNAKE_CASE  )
				snake_case__									=     val
@torch.no_grad()
def      a_ (      _A      ,   _A  )			->			Union[str, Any]:







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

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

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

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

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

				# Verify outputs
				if mlm_model:
								snake_case__									=     torch.Size([1, 11, 30522]  )
								snake_case__									=     torch.tensor([-12.5061, -12.5123, -12.5174]  )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]      ,   __SCREAMING_SNAKE_CASE      ,   atol=1e-4  )

								# verify masked token prediction equals "cats"
								snake_case__									=     outputs.logits[0, 4, :].argmax(-1  ).item()
								assert tokenizer.decode([predicted_id]  ) == "cats"
				elif vqa_model:
								snake_case__									=     torch.Size([1, 3129]  )
								snake_case__									=     torch.tensor([-15.9495, -18.1472, -10.3041]  )
								assert torch.allclose(outputs.logits[0, :3]      ,   __SCREAMING_SNAKE_CASE      ,   atol=1e-4  )
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]      ,   __SCREAMING_SNAKE_CASE      ,   atol=1e-4  )

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

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


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					:		Optional[int]	  =						parser.parse_args()
							convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








from argparse import ArgumentParser

from .env import EnvironmentCommand
def      a_ (      )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     ArgumentParser('Diffusers CLI tool'      ,   usage='diffusers-cli <command> [<args>]'  )
				snake_case__									=     parser.add_subparsers(help='diffusers-cli command helpers'  )

				# Register commands
				EnvironmentCommand.register_subcommand(_A  )

				# Let's go
				snake_case__									=     parser.parse_args()

				if not hasattr(_A      ,   'func'  ):
								parser.print_help()
								exit(1  )

				# Run
				snake_case__									=     args.func(_A  )
				service.run()


if __name__ == "__main__":
							main()




