






"""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         =							logging.get_logger(__name__)






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






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Any	    =   ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						List[Any]	    =   dct.pop(snake_case__					)
	A_		:						Optional[int]	    =   val






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						List[Any]	    =   ViltConfig(image_size=384   ,   patch_size=32   ,   tie_word_embeddings=snake_case__					)
	A_		:						Union[str, Any]	    =   False
	A_		:						str	    =   False
	A_		:						Optional[int]	    =   False
	A_		:						Dict	    =   False
	if "vqa" in checkpoint_url:
		A_		:						Optional[int]	    =   True
		A_		:						Optional[int]	    =   3_129
		A_		:						Optional[int]	    =   """huggingface/label-files"""
		A_		:						str	    =   """vqa2-id2label.json"""
		A_		:						List[Any]	    =   json.load(open(hf_hub_download(snake_case__   ,   snake_case__   ,   repo_type="""dataset"""					)   ,   """r"""					)					)
		A_		:						Dict	    =   {int(snake_case__					): v for k, v in idalabel.items()}
		A_		:						int	    =   idalabel
		A_		:						List[str]	    =   {v: k for k, v in idalabel.items()}
		A_		:						Tuple	    =   ViltForQuestionAnswering(snake_case__					)
	elif "nlvr" in checkpoint_url:
		A_		:						int	    =   True
		A_		:						Union[str, Any]	    =   2
		A_		:						Optional[int]	    =   {0: """False""", 1: """True"""}
		A_		:						Union[str, Any]	    =   {v: k for k, v in config.idalabel.items()}
		A_		:						Optional[Any]	    =   3
		A_		:						int	    =   ViltForImagesAndTextClassification(snake_case__					)
	elif "irtr" in checkpoint_url:
		A_		:						Dict	    =   True
		A_		:						List[Any]	    =   ViltForImageAndTextRetrieval(snake_case__					)
	elif "mlm_itm" in checkpoint_url:
		A_		:						Any	    =   True
		A_		:						Union[str, Any]	    =   ViltForMaskedLM(snake_case__					)
	else:
		raise ValueError("""Unknown model type"""					)

	# load state_dict of original model, remove and rename some keys
	A_		:						Union[str, Any]	    =   torch.hub.load_state_dict_from_url(snake_case__   ,   map_location="""cpu"""					)["""state_dict"""]
	A_		:						List[str]	    =   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_		:						int	    =   ["""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_		:						Dict	    =   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_		:						str	    =   ViltImageProcessor(size=384					)
	A_		:						Tuple	    =   BertTokenizer.from_pretrained("""bert-base-uncased"""					)
	A_		:						Optional[int]	    =   ViltProcessor(snake_case__   ,   snake_case__					)

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

	# Verify outputs
	if mlm_model:
		A_		:						List[Any]	    =   torch.Size([1, 11, 30_522]					)
		A_		:						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]   ,   snake_case__   ,   atol=1E-4					)

		# verify masked token prediction equals "cats"
		A_		:						Any	    =   outputs.logits[0, 4, :].argmax(-1					).item()
		assert tokenizer.decode([predicted_id]					) == "cats"
	elif vqa_model:
		A_		:						int	    =   torch.Size([1, 3_129]					)
		A_		:						List[str]	    =   torch.tensor([-15.9_495, -18.1_472, -10.3_041]					)
		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_		:						Union[str, Any]	    =   outputs.logits.argmax(-1					).item()
		assert model.config.idalabel[predicted_idx] == "2"
	elif nlvr_model:
		A_		:						List[Any]	    =   torch.Size([1, 2]					)
		A_		:						List[str]	    =   torch.tensor([-2.8_721, 2.1_291]					)
		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__":
							_lowerCAmelCase         =							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         =							parser.parse_args()
							convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				FunnelTokenizer
	_A       :				Union[str, Any]    =				FunnelTokenizerFast
	_A       :				List[str]    =				True
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						Tuple	    =   [
		    """<unk>""",
		    """<cls>""",
		    """<sep>""",
		    """want""",
		    """##want""",
		    """##ed""",
		    """wa""",
		    """un""",
		    """runn""",
		    """##ing""",
		    """,""",
		    """low""",
		    """lowest""",
		]
		A_		:						str	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""							) as vocab_writer:
			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   """UNwant\u00E9d,running"""
		A_		:						int	    =   """unwanted, running"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   self.tokenizer_class(self.vocab_file							)

		A_		:						Any	    =   tokenizer.tokenize("""UNwant\u00E9d,running"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]							)
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [7, 4, 5, 10, 8, 9]							)





	def    lowerCamelCase(self							):
		A_		:						List[Any]	    =   self.get_tokenizers(do_lower_case=lowerCAmelCase_							)
		for tokenizer in tokenizers:
			A_		:						Any	    =   tokenizer("""UNwant\u00E9d,running"""							)
			A_		:						Dict	    =   len(inputs["""input_ids"""]							) - 1
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len							)

			A_		:						Tuple	    =   tokenizer("""UNwant\u00E9d,running"""     ,     """UNwant\u00E9d,running"""							)
			self.assertListEqual(inputs["""token_type_ids"""]     ,     [2] + [0] * sentence_len + [1] * sentence_len							)

