




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







def 		lowercase__   (		__A:	List[Any]							,__A:	List[Any]=False							,__A:	Tuple=False							,__A:	Dict=False				):





					'''simple docstring'''






					__magic_name__							:	Union[str, 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 		lowercase__   (		__A:	List[Any]							,__A:	Any				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	Dict				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(__A							,__A				)







def 		lowercase__   (		__A:	List[Any]							,__A:	Tuple							,__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	str    							=							dct.pop(__A				)
					__magic_name__							:	Optional[Any]    							=							val







@torch.no_grad()
def 		lowercase__   (		__A:	str							,__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	str    							=							ViltConfig(image_size=3_8_4							,patch_size=3_2							,tie_word_embeddings=__A				)
					__magic_name__							:	Tuple    							=							False
					__magic_name__							:	Union[str, Any]    							=							False
					__magic_name__							:	Tuple    							=							False
					__magic_name__							:	Optional[Any]    							=							False
					if "vqa" in checkpoint_url:
										__magic_name__							:	Dict    							=							True
										__magic_name__							:	List[Any]    							=							3_1_2_9
										__magic_name__							:	str    							=							'''huggingface/label-files'''
										__magic_name__							:	Tuple    							=							'''vqa2-id2label.json'''
										__magic_name__							:	int    							=							json.load(open(hf_hub_download(__A							,__A							,repo_type='''dataset'''				)							,'''r'''				)				)
										__magic_name__							:	str    							=							{int(__A				): v for k, v in idalabel.items()}
										__magic_name__							:	Optional[Any]    							=							idalabel
										__magic_name__							:	Optional[Any]    							=							{v: k for k, v in idalabel.items()}
										__magic_name__							:	Dict    							=							ViltForQuestionAnswering(__A				)
					elif "nlvr" in checkpoint_url:
										__magic_name__							:	Optional[Any]    							=							True
										__magic_name__							:	Optional[int]    							=							2
										__magic_name__							:	Optional[Any]    							=							{0: '''False''', 1: '''True'''}
										__magic_name__							:	Tuple    							=							{v: k for k, v in config.idalabel.items()}
										__magic_name__							:	Optional[int]    							=							3
										__magic_name__							:	int    							=							ViltForImagesAndTextClassification(__A				)
					elif "irtr" in checkpoint_url:
										__magic_name__							:	int    							=							True
										__magic_name__							:	Dict    							=							ViltForImageAndTextRetrieval(__A				)
					elif "mlm_itm" in checkpoint_url:
										__magic_name__							:	Any    							=							True
										__magic_name__							:	Optional[int]    							=							ViltForMaskedLM(__A				)
					else:
										raise ValueError('''Unknown model type'''				)

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

					# Define processor
					__magic_name__							:	Tuple    							=							ViltImageProcessor(size=3_8_4				)
					__magic_name__							:	Union[str, Any]    							=							BertTokenizer.from_pretrained('''bert-base-uncased'''				)
					__magic_name__							:	Tuple    							=							ViltProcessor(__A							,__A				)

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

					# Verify outputs
					if mlm_model:
										__magic_name__							:	int    							=							torch.Size([1, 1_1, 3_0_5_2_2]				)
										__magic_name__							:	Tuple    							=							torch.tensor([-12.5061, -12.5123, -12.5174]				)
										assert outputs.logits.shape == expected_shape
										assert torch.allclose(outputs.logits[0, 0, :3]							,__A							,atol=1e-4				)

										# verify masked token prediction equals "cats"
										__magic_name__							:	List[str]    							=							outputs.logits[0, 4, :].argmax(-1				).item()
										assert tokenizer.decode([predicted_id]				) == "cats"
					elif vqa_model:
										__magic_name__							:	List[Any]    							=							torch.Size([1, 3_1_2_9]				)
										__magic_name__							:	Any    							=							torch.tensor([-15.9495, -18.1472, -10.3041]				)
										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"
										__magic_name__							:	int    							=							outputs.logits.argmax(-1				).item()
										assert model.config.idalabel[predicted_idx] == "2"
					elif nlvr_model:
										__magic_name__							:	List[str]    							=							torch.Size([1, 2]				)
										__magic_name__							:	Optional[Any]    							=							torch.tensor([-2.8721, 2.1291]				)
										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   :						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   :						List[Any]				=			parser.parse_args()
	convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[Any]				=			{
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSpeech2TextForConditionalGeneration''',
	    '''TFSpeech2TextModel''',
	    '''TFSpeech2TextPreTrainedModel''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''Speech2TextForConditionalGeneration''',
	    '''Speech2TextModel''',
	    '''Speech2TextPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						List[str]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



