import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					logging.get_logger("transformers.models.encodec")

SCREAMING_SNAKE_CASE__		:					Tuple						=					{
    "quantizer.vq.layers.*._codebook.inited": "quantizer.layers.*.codebook.inited",
    "quantizer.vq.layers.*._codebook.cluster_size": "quantizer.layers.*.codebook.cluster_size",
    "quantizer.vq.layers.*._codebook.embed": "quantizer.layers.*.codebook.embed",
    "quantizer.vq.layers.*._codebook.embed_avg": "quantizer.layers.*.codebook.embed_avg",
}
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					{
    "encoder.model.0.conv.conv": "encoder.layers.0.conv",
    "encoder.model.1.block.1.conv.conv": "encoder.layers.1.block.1.conv",
    "encoder.model.1.block.3.conv.conv": "encoder.layers.1.block.3.conv",
    "encoder.model.1.shortcut.conv.conv": "encoder.layers.1.shortcut.conv",
    "encoder.model.3.conv.conv": "encoder.layers.3.conv",
    "encoder.model.4.block.1.conv.conv": "encoder.layers.4.block.1.conv",
    "encoder.model.4.block.3.conv.conv": "encoder.layers.4.block.3.conv",
    "encoder.model.4.shortcut.conv.conv": "encoder.layers.4.shortcut.conv",
    "encoder.model.6.conv.conv": "encoder.layers.6.conv",
    "encoder.model.7.block.1.conv.conv": "encoder.layers.7.block.1.conv",
    "encoder.model.7.block.3.conv.conv": "encoder.layers.7.block.3.conv",
    "encoder.model.7.shortcut.conv.conv": "encoder.layers.7.shortcut.conv",
    "encoder.model.9.conv.conv": "encoder.layers.9.conv",
    "encoder.model.10.block.1.conv.conv": "encoder.layers.10.block.1.conv",
    "encoder.model.10.block.3.conv.conv": "encoder.layers.10.block.3.conv",
    "encoder.model.10.shortcut.conv.conv": "encoder.layers.10.shortcut.conv",
    "encoder.model.12.conv.conv": "encoder.layers.12.conv",
    "encoder.model.13.lstm": "encoder.layers.13.lstm",
    "encoder.model.15.conv.conv": "encoder.layers.15.conv",
}
SCREAMING_SNAKE_CASE__		:					str						=					{
    "encoder.model.0.conv.norm": "encoder.layers.0.norm",
    "encoder.model.1.block.1.conv.norm": "encoder.layers.1.block.1.norm",
    "encoder.model.1.block.3.conv.norm": "encoder.layers.1.block.3.norm",
    "encoder.model.1.shortcut.conv.norm": "encoder.layers.1.shortcut.norm",
    "encoder.model.3.conv.norm": "encoder.layers.3.norm",
    "encoder.model.4.block.1.conv.norm": "encoder.layers.4.block.1.norm",
    "encoder.model.4.block.3.conv.norm": "encoder.layers.4.block.3.norm",
    "encoder.model.4.shortcut.conv.norm": "encoder.layers.4.shortcut.norm",
    "encoder.model.6.conv.norm": "encoder.layers.6.norm",
    "encoder.model.7.block.1.conv.norm": "encoder.layers.7.block.1.norm",
    "encoder.model.7.block.3.conv.norm": "encoder.layers.7.block.3.norm",
    "encoder.model.7.shortcut.conv.norm": "encoder.layers.7.shortcut.norm",
    "encoder.model.9.conv.norm": "encoder.layers.9.norm",
    "encoder.model.10.block.1.conv.norm": "encoder.layers.10.block.1.norm",
    "encoder.model.10.block.3.conv.norm": "encoder.layers.10.block.3.norm",
    "encoder.model.10.shortcut.conv.norm": "encoder.layers.10.shortcut.norm",
    "encoder.model.12.conv.norm": "encoder.layers.12.norm",
    "encoder.model.15.conv.norm": "encoder.layers.15.norm",
}
SCREAMING_SNAKE_CASE__		:					Any						=					{
    "decoder.model.0.conv.conv": "decoder.layers.0.conv",
    "decoder.model.1.lstm": "decoder.layers.1.lstm",
    "decoder.model.3.convtr.convtr": "decoder.layers.3.conv",
    "decoder.model.4.block.1.conv.conv": "decoder.layers.4.block.1.conv",
    "decoder.model.4.block.3.conv.conv": "decoder.layers.4.block.3.conv",
    "decoder.model.4.shortcut.conv.conv": "decoder.layers.4.shortcut.conv",
    "decoder.model.6.convtr.convtr": "decoder.layers.6.conv",
    "decoder.model.7.block.1.conv.conv": "decoder.layers.7.block.1.conv",
    "decoder.model.7.block.3.conv.conv": "decoder.layers.7.block.3.conv",
    "decoder.model.7.shortcut.conv.conv": "decoder.layers.7.shortcut.conv",
    "decoder.model.9.convtr.convtr": "decoder.layers.9.conv",
    "decoder.model.10.block.1.conv.conv": "decoder.layers.10.block.1.conv",
    "decoder.model.10.block.3.conv.conv": "decoder.layers.10.block.3.conv",
    "decoder.model.10.shortcut.conv.conv": "decoder.layers.10.shortcut.conv",
    "decoder.model.12.convtr.convtr": "decoder.layers.12.conv",
    "decoder.model.13.block.1.conv.conv": "decoder.layers.13.block.1.conv",
    "decoder.model.13.block.3.conv.conv": "decoder.layers.13.block.3.conv",
    "decoder.model.13.shortcut.conv.conv": "decoder.layers.13.shortcut.conv",
    "decoder.model.15.conv.conv": "decoder.layers.15.conv",
}
SCREAMING_SNAKE_CASE__		:					str						=					{
    "decoder.model.0.conv.norm": "decoder.layers.0.norm",
    "decoder.model.3.convtr.norm": "decoder.layers.3.norm",
    "decoder.model.4.block.1.conv.norm": "decoder.layers.4.block.1.norm",
    "decoder.model.4.block.3.conv.norm": "decoder.layers.4.block.3.norm",
    "decoder.model.4.shortcut.conv.norm": "decoder.layers.4.shortcut.norm",
    "decoder.model.6.convtr.norm": "decoder.layers.6.norm",
    "decoder.model.7.block.1.conv.norm": "decoder.layers.7.block.1.norm",
    "decoder.model.7.block.3.conv.norm": "decoder.layers.7.block.3.norm",
    "decoder.model.7.shortcut.conv.norm": "decoder.layers.7.shortcut.norm",
    "decoder.model.9.convtr.norm": "decoder.layers.9.norm",
    "decoder.model.10.block.1.conv.norm": "decoder.layers.10.block.1.norm",
    "decoder.model.10.block.3.conv.norm": "decoder.layers.10.block.3.norm",
    "decoder.model.10.shortcut.conv.norm": "decoder.layers.10.shortcut.norm",
    "decoder.model.12.convtr.norm": "decoder.layers.12.norm",
    "decoder.model.13.block.1.conv.norm": "decoder.layers.13.block.1.norm",
    "decoder.model.13.block.3.conv.norm": "decoder.layers.13.block.3.norm",
    "decoder.model.13.shortcut.conv.norm": "decoder.layers.13.shortcut.norm",
    "decoder.model.15.conv.norm": "decoder.layers.15.norm",
}
SCREAMING_SNAKE_CASE__		:					str						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE__		:					List[str]						=					[]
SCREAMING_SNAKE_CASE__		:					Dict						=					[]







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			int     ,  lowercase__  :			Any     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			List[Any] ):







					'''simple docstring'''


					for attribute in key.split('.' ):
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		getattr(lowercase__     ,  lowercase__ )

					if weight_type is not None:
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		getattr(lowercase__     ,  lowercase__ ).shape
					else:
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
										    f''' {value.shape} for {full_name}''' )

					if weight_type == "weight":
										SCREAMING_SNAKE_CASE__				:				int			=		value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		value
					elif weight_type == "running_mean":
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		value
					elif weight_type == "running_var":
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		value
					elif weight_type == "num_batches_tracked":
										SCREAMING_SNAKE_CASE__				:				Dict			=		value
					elif weight_type == "weight_ih_l0":
										SCREAMING_SNAKE_CASE__				:				str			=		value
					elif weight_type == "weight_hh_l0":
										SCREAMING_SNAKE_CASE__				:				Dict			=		value
					elif weight_type == "bias_ih_l0":
										SCREAMING_SNAKE_CASE__				:				Tuple			=		value
					elif weight_type == "bias_hh_l0":
										SCREAMING_SNAKE_CASE__				:				int			=		value
					elif weight_type == "weight_ih_l1":
										SCREAMING_SNAKE_CASE__				:				str			=		value
					elif weight_type == "weight_hh_l1":
										SCREAMING_SNAKE_CASE__				:				Any			=		value
					elif weight_type == "bias_ih_l1":
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		value
					elif weight_type == "bias_hh_l1":
										SCREAMING_SNAKE_CASE__				:				str			=		value
					else:
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		value

					logger.info(f'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )







def     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			Tuple ):







					'''simple docstring'''


					for key in ignore_keys:
										if key.endswith('.*' ):
															if name.startswith(key[:-1] ):
																				return True
										elif ".*." in key:
															SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		key.split('.*.' )
															if prefix in name and suffix in name:
																				return True
										elif key in name:
															return True
					return False







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			Optional[int] ):







					'''simple docstring'''


					SCREAMING_SNAKE_CASE__				:				Tuple			=		[]

					if model_name == "encodec_24khz" or "encodec_32khz":
										SCREAMING_SNAKE_CASE__				:				Tuple			=		MAPPING_24K
					elif model_name == "encodec_48khz":
										SCREAMING_SNAKE_CASE__				:				str			=		MAPPING_48K
					else:
										raise ValueError(f'''Unsupported model: {model_name}''' )

					for name, value in orig_dict.items():
										if should_ignore(lowercase__     ,  lowercase__ ):
															logger.info(f'''{name} was ignored''' )
															continue

										SCREAMING_SNAKE_CASE__				:				Any			=		False
										for key, mapped_key in MAPPING.items():
															if "*" in key:
																				SCREAMING_SNAKE_CASE__				:				int			=		key.split('.*.' )
																				if prefix in name and suffix in name:
																									SCREAMING_SNAKE_CASE__				:				Dict			=		suffix

															if key in name:
																				# HACK otherwise .embed gets initialized with .embed_avg too
																				if key.endswith('embed' ) and name.endswith('embed_avg' ):
																									continue

																				SCREAMING_SNAKE_CASE__				:				int			=		True
																				if "*" in mapped_key:
																									SCREAMING_SNAKE_CASE__				:				str			=		name.split(lowercase__ )[0].split('.' )[-2]
																									SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		mapped_key.replace('*'     ,  lowercase__ )
																				if "weight_g" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'weight_g'
																				elif "weight_v" in name:
																									SCREAMING_SNAKE_CASE__				:				Tuple			=		'weight_v'
																				elif "weight_ih_l0" in name:
																									SCREAMING_SNAKE_CASE__				:				List[Any]			=		'weight_ih_l0'
																				elif "weight_hh_l0" in name:
																									SCREAMING_SNAKE_CASE__				:				List[Any]			=		'weight_hh_l0'
																				elif "bias_ih_l0" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'bias_ih_l0'
																				elif "bias_hh_l0" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'bias_hh_l0'
																				elif "weight_ih_l1" in name:
																									SCREAMING_SNAKE_CASE__				:				Dict			=		'weight_ih_l1'
																				elif "weight_hh_l1" in name:
																									SCREAMING_SNAKE_CASE__				:				Tuple			=		'weight_hh_l1'
																				elif "bias_ih_l1" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'bias_ih_l1'
																				elif "bias_hh_l1" in name:
																									SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'bias_hh_l1'
																				elif "bias" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'bias'
																				elif "weight" in name:
																									SCREAMING_SNAKE_CASE__				:				Optional[int]			=		'weight'
																				elif "running_mean" in name:
																									SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'running_mean'
																				elif "running_var" in name:
																									SCREAMING_SNAKE_CASE__				:				List[str]			=		'running_var'
																				elif "num_batches_tracked" in name:
																									SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'num_batches_tracked'
																				else:
																									SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
																				set_recursively(lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__ )
															continue
										if not is_used:
															unused_weights.append(lowercase__ )

					logger.warning(f'''Unused weights: {unused_weights}''' )







@torch.no_grad()
def     _a				(      lowercase__  :			Tuple     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			List[str]     ,  lowercase__  :			Optional[Any]=None     ,  lowercase__  :			List[Any]=None     ,  ):







					'''simple docstring'''


					if config_path is not None:
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		EncodecConfig.from_pretrained(lowercase__ )
					else:
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[8, 5, 4, 4]
										SCREAMING_SNAKE_CASE__				:				List[Any]			=		[2.2]
										SCREAMING_SNAKE_CASE__				:				Dict			=		64
										SCREAMING_SNAKE_CASE__				:				Dict			=		3_20_00
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		20_48
										SCREAMING_SNAKE_CASE__				:				Tuple			=		False
										SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		False
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False
					elif model_name == "encodec_48khz":
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[8, 5, 4, 2]
										SCREAMING_SNAKE_CASE__				:				Any			=		[3.0, 6.0, 12.0, 24.0]
										SCREAMING_SNAKE_CASE__				:				int			=		4_80_00
										SCREAMING_SNAKE_CASE__				:				List[str]			=		2
										SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False
										SCREAMING_SNAKE_CASE__				:				Tuple			=		'time_group_norm'
										SCREAMING_SNAKE_CASE__				:				int			=		True
										SCREAMING_SNAKE_CASE__				:				List[str]			=		1.0
										SCREAMING_SNAKE_CASE__				:				Tuple			=		0.01
					else:
										raise ValueError(f'''Unknown model name: {model_name}''' )

					SCREAMING_SNAKE_CASE__				:				Any			=		EncodecModel(lowercase__ )

					SCREAMING_SNAKE_CASE__				:				int			=		EncodecFeatureExtractor(
					    feature_size=config.audio_channels     ,  sampling_rate=config.sampling_rate     ,  chunk_length_s=config.chunk_length_s     ,  overlap=config.overlap     ,  )
					feature_extractor.save_pretrained(lowercase__ )

					SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.load(lowercase__ )
					if "best_state" in original_checkpoint:
										# we might have a training state saved, in which case discard the yaml results and just retain the weights
										SCREAMING_SNAKE_CASE__				:				str			=		original_checkpoint['best_state']
					recursively_load_weights(lowercase__     ,  lowercase__     ,  lowercase__ )
					model.save_pretrained(lowercase__ )

					if repo_id:
										print('Pushing to the hub...' )
										feature_extractor.push_to_hub(lowercase__ )
										model.push_to_hub(lowercase__ )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE__		:					int						=					argparse.ArgumentParser()
						parser.add_argument(
						    "--model",
						    default="encodec_24khz",
						    type=str,
						    help="The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.",
						)
						parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
						parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						parser.add_argument(
						    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
						)
						parser.add_argument(
						    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
						)

						SCREAMING_SNAKE_CASE__		:					Dict						=					parser.parse_args()
						convert_checkpoint(
						    args.model,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.push_to_hub,
						)




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()
SCREAMING_SNAKE_CASE__		:					int						=					logging.get_logger(__name__)







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any]=False     ,  lowercase__  :			str=False     ,  lowercase__  :			Dict=False ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				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     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			Dict ):







     '''simple docstring'''


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







def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(lowercase__     ,  lowercase__ )







def     _a				(      lowercase__  :			int     ,  lowercase__  :			int     ,  lowercase__  :			Dict ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		dct.pop(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Any			=		val







@torch.no_grad()
def     _a				(      lowercase__  :			Dict     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[Any]			=		ViltConfig(image_size=3_84     ,  patch_size=32     ,  tie_word_embeddings=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		False
     SCREAMING_SNAKE_CASE__				:				Tuple			=		False
     SCREAMING_SNAKE_CASE__				:				List[str]			=		False
     SCREAMING_SNAKE_CASE__				:				str			=		False
     if "vqa" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Any			=		True
          SCREAMING_SNAKE_CASE__				:				str			=		31_29
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'huggingface/label-files'
          SCREAMING_SNAKE_CASE__				:				int			=		'vqa2-id2label.json'
          SCREAMING_SNAKE_CASE__				:				str			=		json.load(open(hf_hub_download(lowercase__     ,  lowercase__     ,  repo_type='dataset' )     ,  'r' ) )
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{int(lowercase__ ): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				Dict			=		idalabel
          SCREAMING_SNAKE_CASE__				:				str			=		{v: k for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				List[str]			=		ViltForQuestionAnswering(lowercase__ )
     elif "nlvr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
          SCREAMING_SNAKE_CASE__				:				List[str]			=		2
          SCREAMING_SNAKE_CASE__				:				Dict			=		{0: 'False', 1: 'True'}
          SCREAMING_SNAKE_CASE__				:				Dict			=		{v: k for k, v in config.idalabel.items()}
          SCREAMING_SNAKE_CASE__				:				Tuple			=		3
          SCREAMING_SNAKE_CASE__				:				int			=		ViltForImagesAndTextClassification(lowercase__ )
     elif "irtr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				Dict			=		True
          SCREAMING_SNAKE_CASE__				:				str			=		ViltForImageAndTextRetrieval(lowercase__ )
     elif "mlm_itm" in checkpoint_url:
          SCREAMING_SNAKE_CASE__				:				int			=		True
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ViltForMaskedLM(lowercase__ )
     else:
          raise ValueError('Unknown model type' )

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

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

     # Define processor
     SCREAMING_SNAKE_CASE__				:				str			=		ViltImageProcessor(size=3_84 )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		BertTokenizer.from_pretrained('bert-base-uncased' )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		ViltProcessor(lowercase__     ,  lowercase__ )

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

     # Verify outputs
     if mlm_model:
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.Size([1, 11, 3_05_22] )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.tensor([-12.5061, -12.5123, -12.5174] )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]     ,  lowercase__     ,  atol=1E-4 )

          # verify masked token prediction equals "cats"
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		outputs.logits[0, 4, :].argmax(-1 ).item()
          assert tokenizer.decode([predicted_id] ) == "cats"
     elif vqa_model:
          SCREAMING_SNAKE_CASE__				:				str			=		torch.Size([1, 31_29] )
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([-15.9495, -18.1472, -10.3041] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]     ,  lowercase__     ,  atol=1E-4 )

          # verify vqa prediction equals "2"
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		outputs.logits.argmax(-1 ).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.Size([1, 2] )
          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([-2.8721, 2.1291] )
          assert torch.allclose(outputs.logits[0, :3]     ,  lowercase__     ,  atol=1E-4 )
          assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					str						=					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."
      )

      SCREAMING_SNAKE_CASE__		:					List[Any]						=					parser.parse_args()
      convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



