
"""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()
_lowercase							   =				logging.get_logger(__name__)






def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     int=False	,      snake_case__   :     Dict=False	,      snake_case__   :     Union[str, Any]=False     ):
	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       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     Any     ):
	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       _snake_case				(		snake_case__   :     Optional[Any]     ):
	A						   =   ['head.weight', 'head.bias']
	for k in ignore_keys:
		state_dict.pop(snake_case__	,      snake_case__     )






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Optional[Any]	,      snake_case__   :     str     ):
	A						   =   dct.pop(snake_case__     )
	A						   =   val






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

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

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

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

	# Verify outputs
	if mlm_model:
		A						   =   torch.Size([1, 11, 3_0522]     )
		A						   =   torch.tensor([-12.5061, -12.5123, -12.5174]     )
		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, 0, :3]	,      snake_case__	,      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([-15.9495, -18.1472, -10.3041]     )
		assert torch.allclose(outputs.logits[0, :3]	,      snake_case__	,      atol=1e-4     )
		assert outputs.logits.shape == expected_shape
		assert torch.allclose(outputs.logits[0, 0, :3]	,      snake_case__	,      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([-2.8721, 2.1291]     )
		assert torch.allclose(outputs.logits[0, :3]	,      snake_case__	,      atol=1e-4     )
		assert outputs.logits.shape == expected_shape

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


if __name__ == "__main__":
		_lowercase							   =				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.'''
		)

		_lowercase							   =				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,
)


_lowercase							   =				{
    '''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:
		_lowercase							   =				['''CLIPTokenizerFast''']

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

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''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

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