




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()
a_				:    Optional[Any]										=     logging.get_logger(__name__)





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





def 						UpperCAmelCase				(			A__:    str     )  ->     Any:
			__lowerCamelCase					:	List[str]	       =				['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    Union[str, Any]							,				A__:    Tuple     )  ->     Tuple:
			__lowerCamelCase					:	Optional[Any]	       =				dct.pop(A__     )
			__lowerCamelCase					:	Optional[int]	       =				val





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

			# load state_dict of original model, remove and rename some keys
			__lowerCamelCase					:	str	       =				torch.hub.load_state_dict_from_url(A__							,				map_location='cpu'     )['state_dict']
			__lowerCamelCase					:	Tuple	       =				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:
						__lowerCamelCase					:	List[Any]	       =				['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:
						__lowerCamelCase					:	List[Any]	       =				model.load_state_dict(A__							,				strict=A__     )
						assert missing_keys == ["mlm_score.decoder.bias"]
			else:
						model.load_state_dict(A__     )

			# Define processor
			__lowerCamelCase					:	str	       =				ViltImageProcessor(size=384     )
			__lowerCamelCase					:	Union[str, Any]	       =				BertTokenizer.from_pretrained('bert-base-uncased'     )
			__lowerCamelCase					:	str	       =				ViltProcessor(A__							,				A__     )

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

			# Verify outputs
			if mlm_model:
						__lowerCamelCase					:	int	       =				torch.Size([1, 11, 30522]     )
						__lowerCamelCase					:	int	       =				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]							,				A__							,				atol=1E-4     )

						# verify masked token prediction equals "cats"
						__lowerCamelCase					:	Tuple	       =				outputs.logits[0, 4, :].argmax(-1     ).item()
						assert tokenizer.decode([predicted_id]     ) == "cats"
			elif vqa_model:
						__lowerCamelCase					:	List[Any]	       =				torch.Size([1, 3129]     )
						__lowerCamelCase					:	Union[str, Any]	       =				torch.tensor([-15.9_495, -18.1_472, -10.3_041]     )
						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"
						__lowerCamelCase					:	Any	       =				outputs.logits.argmax(-1     ).item()
						assert model.config.idalabel[predicted_idx] == "2"
			elif nlvr_model:
						__lowerCamelCase					:	Union[str, Any]	       =				torch.Size([1, 2]     )
						__lowerCamelCase					:	Any	       =				torch.tensor([-2.8_721, 2.1_291]     )
						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__":
					a_				:    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.'''
					)

					a_				:    Union[str, Any]										=     parser.parse_args()
					convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



