






"""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        snake_case   (     A__			,A__=False			,A__=False			,A__=False		):

			'''simple docstring'''





			UpperCAmelCase_  :				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        snake_case   (     A__			,A__		):

			'''simple docstring'''





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

def        snake_case   (     A__		):

			'''simple docstring'''





			UpperCAmelCase_  :				Dict      =      ['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__			,lowerCAmelCase__		)

def        snake_case   (     A__			,A__			,A__		):

			'''simple docstring'''





			UpperCAmelCase_  :				Optional[int]      =      dct.pop(lowerCAmelCase__		)
			UpperCAmelCase_  :				Tuple      =      val

@torch.no_grad()
def        snake_case   (     A__			,A__		):

			'''simple docstring'''





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

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

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

			# Define processor
			UpperCAmelCase_  :				Tuple      =      ViltImageProcessor(size=3_84		)
			UpperCAmelCase_  :				Optional[int]      =      BertTokenizer.from_pretrained("bert-base-uncased"		)
			UpperCAmelCase_  :				int      =      ViltProcessor(lowerCAmelCase__			,lowerCAmelCase__		)

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

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

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

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

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


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"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       			=   {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPTokenizerFast''']

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   ['''CLIPFeatureExtractor''']
			lowerCamelCase_       			=   ['''CLIPImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CLIPModel''',
			    '''CLIPPreTrainedModel''',
			    '''CLIPTextModel''',
			    '''CLIPTextModelWithProjection''',
			    '''CLIPVisionModel''',
			    '''CLIPVisionModelWithProjection''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFCLIPModel''',
			    '''TFCLIPPreTrainedModel''',
			    '''TFCLIPTextModel''',
			    '''TFCLIPVisionModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCamelCase_       			=   [
			    '''FlaxCLIPModel''',
			    '''FlaxCLIPPreTrainedModel''',
			    '''FlaxCLIPTextModel''',
			    '''FlaxCLIPTextPreTrainedModel''',
			    '''FlaxCLIPVisionModel''',
			    '''FlaxCLIPVisionPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_clip import (
			    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    CLIPConfig,
			    CLIPOnnxConfig,
			    CLIPTextConfig,
			    CLIPVisionConfig,
			)
			from .processing_clip import CLIPProcessor
			from .tokenization_clip import CLIPTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_clip_fast import CLIPTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_clip import CLIPFeatureExtractor
						from .image_processing_clip import CLIPImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_clip import (
						    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CLIPModel,
						    CLIPPreTrainedModel,
						    CLIPTextModel,
						    CLIPTextModelWithProjection,
						    CLIPVisionModel,
						    CLIPVisionModelWithProjection,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_clip import (
						    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCLIPModel,
						    TFCLIPPreTrainedModel,
						    TFCLIPTextModel,
						    TFCLIPVisionModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_clip import (
						    FlaxCLIPModel,
						    FlaxCLIPPreTrainedModel,
						    FlaxCLIPTextModel,
						    FlaxCLIPTextPreTrainedModel,
						    FlaxCLIPVisionModel,
						    FlaxCLIPVisionPreTrainedModel,
						)


else:
			import sys

			lowerCamelCase_       			=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



