




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		:				Tuple       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Optional[int],  A		:					Any=False,  A		:					int=False,  A		:					Any=False   ):






			'''simple docstring'''







			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 			__magic_name__				( A		:					Optional[Any],  A		:					Optional[int]   ):






			'''simple docstring'''







			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 			__magic_name__				( A		:					Union[str, Any]   ):






			'''simple docstring'''







			a    =			["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(A,  A   )




def 			__magic_name__				( A		:					List[str],  A		:					Any,  A		:					List[Any]   ):






			'''simple docstring'''







			a    =			dct.pop(A   )
			a    =			val




@torch.no_grad()
def 			__magic_name__				( A		:					Optional[int],  A		:					Optional[int]   ):






			'''simple docstring'''







			a    =			ViltConfig(image_size=384,  patch_size=32,  tie_word_embeddings=A   )
			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(A,  A,  repo_type="dataset"   ),  "r"   )   )
						a    =			{int(A   ): v for k, v in idalabel.items()}
						a    =			idalabel
						a    =			{v: k for k, v in idalabel.items()}
						a    =			ViltForQuestionAnswering(A   )
			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(A   )
			elif "irtr" in checkpoint_url:
						a    =			True
						a    =			ViltForImageAndTextRetrieval(A   )
			elif "mlm_itm" in checkpoint_url:
						a    =			True
						a    =			ViltForMaskedLM(A   )
			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(A,  map_location="cpu"   )["state_dict"]
			a    =			create_rename_keys(A,  A,  A,  A   )
			for src, dest in rename_keys:
						rename_key(A,  A,  A   )
			read_in_q_k_v(A,  A   )
			if mlm_model or irtr_model:
						a    =			["itm_score.fc.weight", "itm_score.fc.bias"]
						for k in ignore_keys:
									state_dict.pop(A,  A   )

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

			# Define processor
			a    =			ViltImageProcessor(size=384   )
			a    =			BertTokenizer.from_pretrained("bert-base-uncased"   )
			a    =			ViltProcessor(A,  A   )

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

			# Verify outputs
			if mlm_model:
						a    =			torch.Size([1, 11, 30522]   )
						a    =			torch.tensor([-12.50_61, -12.51_23, -12.51_74]   )
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3],  A,  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([-15.94_95, -18.14_72, -10.30_41]   )
						assert torch.allclose(outputs.logits[0, :3],  A,  atol=1E-4   )
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3],  A,  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([-2.87_21, 2.12_91]   )
						assert torch.allclose(outputs.logits[0, :3],  A,  atol=1E-4   )
						assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
		__lowerCAmelCase		:				Dict       			=		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		:				Optional[int]       			=		parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		__lowerCAmelCase		:				List[Any]       			=		'0.12'  # assumed parallelism: 8


if is_torch_available():
		import torch




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any],  A		:					Optional[int]=None   ):






			'''simple docstring'''







			if rng is None:
						a    =			random.Random()

			a    =			1
			for dim in shape:
						total_dims *= dim

			a    =			[]
			for _ in range(A   ):
						values.append(rng.randint(0,  vocab_size - 1   )   )

			a    =			np.array(A,  dtype=jnp.intaa   ).reshape(A   )

			return output




def 			__magic_name__				( A		:					Dict,  A		:					Union[str, Any]=None   ):






			'''simple docstring'''







			a    =			ids_tensor(A,  vocab_size=2,  rng=A   )
			# make sure that at least one token is attended to for each batch
			a    =			1
			return attn_mask




@require_flax
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							List[str]							 =					None
				SCREAMING_SNAKE_CASE_       :							Any							 =					()





				def 							__UpperCAmelCase							(      self				:     int							)					->     List[str]:
							a    ,		a    =			self.model_tester.prepare_config_and_inputs_for_common()

							# cut to half length & take max batch_size 3
							a    =			2
							a    =			inputs["input_ids"].shape[-1] // 2
							a    =			inputs["input_ids"][:max_batch_size, :sequence_length]

							a    =			jnp.ones_like(__lowerCamelCase							)
							a    =			attention_mask[:max_batch_size, :sequence_length]

							# generate max 5 tokens
							a    =			input_ids.shape[-1] + 5
							if config.eos_token_id is not None and config.pad_token_id is None:
										# hack to allow generate for models such as GPT2 as is done in `generate()`
										a    =			config.eos_token_id
							return config, input_ids, attention_mask, max_length





				@is_pt_flax_cross_test
				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     int:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			0

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
										a    =			getattr(__lowerCamelCase					,      __lowerCamelCase							)
										a    =			pt_model_class(__lowerCamelCase							).eval()
										a    =			load_flax_weights_in_pytorch_model(__lowerCamelCase					,      flax_model.params							)

										a    =			flax_model.generate(__lowerCamelCase							).sequences
										a    =			pt_model.generate(torch.tensor(__lowerCamelCase					,      dtype=torch.long							)							)

										if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
													a    =			flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

										self.assertListEqual(pt_generation_outputs.numpy().tolist()					,      flax_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Optional[int]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Any:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			False
							a    =			max_length
							a    =			2
							a    =			2

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[0]					,      input_ids.shape[0] * config.num_return_sequences							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			True
							a    =			max_length
							a    =			0.8
							a    =			10
							a    =			0.3
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     List[str]							)					->     Union[str, Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()
							a    =			max_length
							a    =			2
							a    =			1
							a    =			8
							a    =			9

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			False
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Tuple:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			True
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     List[Any]:
							a    ,		a    ,		a    ,		a    =			self._get_input_ids_and_config()

							# pad attention mask on the left
							a    =			attention_mask.at[(0, 0)].set(0							)

							a    =			2
							a    =			max_length

							for model_class in self.all_generative_model_classes:
										a    =			model_class(__lowerCamelCase							)

										a    =			model.generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences
										self.assertEqual(generation_outputs.shape[-1]					,      __lowerCamelCase							)

										a    =			jit(model.generate							)
										a    =			jit_generate(__lowerCamelCase					,      attention_mask=__lowerCamelCase							).sequences

										self.assertListEqual(generation_outputs.tolist()					,      jit_generation_outputs.tolist()							)




@require_flax
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Optional[Any]:
							a    =			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"							)
							a    =			FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"							)

							a    =			"Hello world"
							a    =			tokenizer(__lowerCamelCase					,      return_tensors="np"							).input_ids

							# typos are quickly detected (the correct argument is `do_sample`)
							with self.assertRaisesRegex(__lowerCamelCase					,      "do_samples"							):
										model.generate(__lowerCamelCase					,      do_samples=__lowerCamelCase							)

							# arbitrary arguments that will not be used anywhere are also not accepted
							with self.assertRaisesRegex(__lowerCamelCase					,      "foo"							):
										a    =			{"foo": "bar"}
										model.generate(__lowerCamelCase					,      **__lowerCamelCase							)






