




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   :	Union[str, Any]       =    logging.get_logger(__name__)




def 				_UpperCAmelCase	(UpperCamelCase_			:			Optional[int]   ,     UpperCamelCase_			:			str=False   ,     UpperCamelCase_			:			Tuple=False   ,     UpperCamelCase_			:			List[str]=False							):
				'''simple docstring'''
				_lowerCAmelCase     :     List[str]              =   []
				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 				_UpperCAmelCase	(UpperCamelCase_			:			str   ,     UpperCamelCase_			:			str							):
				'''simple docstring'''






				for i in range(config.num_hidden_layers							):
								_lowerCAmelCase     :     Tuple              =   """vilt."""
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								_lowerCAmelCase     :     List[str]              =   state_dict.pop(F"transformer.blocks.{i}.attn.qkv.weight"							)
								_lowerCAmelCase     :     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
								_lowerCAmelCase     :     List[str]              =   in_proj_weight[
								    : config.hidden_size, :
								]
								_lowerCAmelCase     :     Optional[Any]              =   in_proj_bias[: config.hidden_size]
								_lowerCAmelCase     :     Dict              =   in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								_lowerCAmelCase     :     Tuple              =   in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								_lowerCAmelCase     :     Union[str, Any]              =   in_proj_weight[
								    -config.hidden_size :, :
								]
								_lowerCAmelCase     :     Any              =   in_proj_bias[-config.hidden_size :]




def 				_UpperCAmelCase	(UpperCamelCase_			:			Tuple							):
				'''simple docstring'''
				_lowerCAmelCase     :     Union[str, Any]              =   ["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(UpperCamelCase__   ,     UpperCamelCase__							)




def 				_UpperCAmelCase	(UpperCamelCase_			:			Any   ,     UpperCamelCase_			:			List[Any]   ,     UpperCamelCase_			:			Tuple							):
				'''simple docstring'''
				_lowerCAmelCase     :     Optional[Any]              =   dct.pop(UpperCamelCase__							)
				_lowerCAmelCase     :     List[str]              =   val




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

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

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

				# Define processor
				_lowerCAmelCase     :     Optional[int]              =   ViltImageProcessor(size=384							)
				_lowerCAmelCase     :     Optional[Any]              =   BertTokenizer.from_pretrained("""bert-base-uncased"""							)
				_lowerCAmelCase     :     str              =   ViltProcessor(UpperCamelCase__   ,     UpperCamelCase__							)

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

				# Verify outputs
				if mlm_model:
								_lowerCAmelCase     :     str              =   torch.Size([1, 11, 30522]							)
								_lowerCAmelCase     :     Any              =   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]   ,     UpperCamelCase__   ,     atol=1E-4							)

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

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

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


if __name__ == "__main__":
	_lowerCamelCase   :	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."
	)

	_lowerCamelCase   :	Any       =    parser.parse_args()
	convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_UpperCAmelCase						:  Dict									=			"""sshleifer/bart-tiny-random"""
_UpperCAmelCase						:  Optional[Any]									=			"""patrickvonplaten/t5-tiny-random"""







@require_torch
class 					lowercase							(			unittest.TestCase     ):




				@cached_property
				def        a				(   self			):
											return AutoConfig.from_pretrained(snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.num_hidden_layers ,  1			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=snake_case			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  self.teacher_config.encoder_layers			)




				def        a				(   self			):
											snake_case_							,       *snake_case_     							=       create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=1 ,  d=1			)
											self.assertEqual(student.config.encoder_layers ,  1			)
											self.assertEqual(student.config.decoder_layers ,  1			)




				def        a				(   self			):
											with self.assertRaises(snake_case			):
																		create_student_by_copying_alternating_layers(snake_case ,  tempfile.mkdtemp() ,  e=snake_case ,  d=snake_case			)




