
"""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"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )