


'''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()
__UpperCAmelCase					       =							logging.get_logger(__name__)


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


def       _snake_case     (  A )		->					List[Any]:
							lowerCAmelCase__      =			['''head.weight''', '''head.bias''']
							for k in ignore_keys:
														state_dict.pop(A				,					A )


def       _snake_case     (  A				,					A				,					A )		->					List[Any]:
							lowerCAmelCase__      =			dct.pop(A )
							lowerCAmelCase__      =			val


@torch.no_grad()
def       _snake_case     (  A				,					A )		->					List[str]:
							lowerCAmelCase__      =			ViltConfig(image_size=384				,					patch_size=32				,					tie_word_embeddings=A )
							lowerCAmelCase__      =			False
							lowerCAmelCase__      =			False
							lowerCAmelCase__      =			False
							lowerCAmelCase__      =			False
							if "vqa" in checkpoint_url:
														lowerCAmelCase__      =			True
														lowerCAmelCase__      =			3129
														lowerCAmelCase__      =			'''huggingface/label-files'''
														lowerCAmelCase__      =			'''vqa2-id2label.json'''
														lowerCAmelCase__      =			json.load(open(hf_hub_download(A				,					A				,					repo_type='''dataset''' )				,					'''r''' ) )
														lowerCAmelCase__      =			{int(A ): v for k, v in idalabel.items()}
														lowerCAmelCase__      =			idalabel
														lowerCAmelCase__      =			{v: k for k, v in idalabel.items()}
														lowerCAmelCase__      =			ViltForQuestionAnswering(A )
							elif "nlvr" in checkpoint_url:
														lowerCAmelCase__      =			True
														lowerCAmelCase__      =			2
														lowerCAmelCase__      =			{0: '''False''', 1: '''True'''}
														lowerCAmelCase__      =			{v: k for k, v in config.idalabel.items()}
														lowerCAmelCase__      =			3
														lowerCAmelCase__      =			ViltForImagesAndTextClassification(A )
							elif "irtr" in checkpoint_url:
														lowerCAmelCase__      =			True
														lowerCAmelCase__      =			ViltForImageAndTextRetrieval(A )
							elif "mlm_itm" in checkpoint_url:
														lowerCAmelCase__      =			True
														lowerCAmelCase__      =			ViltForMaskedLM(A )
							else:
														raise ValueError('''Unknown model type''' )

							# load state_dict of original model, remove and rename some keys
							lowerCAmelCase__      =			torch.hub.load_state_dict_from_url(A				,					map_location='''cpu''' )['''state_dict''']
							lowerCAmelCase__      =			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:
														lowerCAmelCase__      =			['''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:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			model.load_state_dict(A				,					strict=A )
														assert missing_keys == ["mlm_score.decoder.bias"]
							else:
														model.load_state_dict(A )

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

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

							# Verify outputs
							if mlm_model:
														lowerCAmelCase__      =			torch.Size([1, 11, 30522] )
														lowerCAmelCase__      =			torch.tensor([-12.5_061, -12.5_123, -12.5_174] )
														assert outputs.logits.shape == expected_shape
														assert torch.allclose(outputs.logits[0, 0, :3]				,					A				,					atol=1E-4 )

														# verify masked token prediction equals "cats"
														lowerCAmelCase__      =			outputs.logits[0, 4, :].argmax(-1 ).item()
														assert tokenizer.decode([predicted_id] ) == "cats"
							elif vqa_model:
														lowerCAmelCase__      =			torch.Size([1, 3129] )
														lowerCAmelCase__      =			torch.tensor([-15.9_495, -18.1_472, -10.3_041] )
														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"
														lowerCAmelCase__      =			outputs.logits.argmax(-1 ).item()
														assert model.config.idalabel[predicted_idx] == "2"
							elif nlvr_model:
														lowerCAmelCase__      =			torch.Size([1, 2] )
														lowerCAmelCase__      =			torch.tensor([-2.8_721, 2.1_291] )
														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__":
		__UpperCAmelCase					       =							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.'''
		)

		__UpperCAmelCase					       =							parser.parse_args()
		convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''






from collections import Counter
from timeit import timeit


def       _snake_case     (  A = ""				,					)		->					bool:
							return sum(c % 2 for c in Counter(input_str.replace(''' '''				,					'''''' ).lower() ).values() ) < 2


def       _snake_case     (  A = "" )		->					bool:
							if len(A ) == 0:
														return True
							lowerCAmelCase__      =			input_str.replace(''' '''				,					'''''' ).lower()
							# character_freq_dict: Stores the frequency of every character in the input string
							lowerCAmelCase__      =			{}

							for character in lower_case_input_str:
														lowerCAmelCase__      =			character_freq_dict.get(A				,					0 ) + 1
							lowerCAmelCase__      =			0

							for character_count in character_freq_dict.values():
														if character_count % 2:
																					odd_char += 1
							if odd_char > 1:
														return False
							return True


def       _snake_case     (  A = "" )		->					None:
							print('''\nFor string = '''				,					A				,					''':''' )
							print(
							    '''> can_string_be_rearranged_as_palindrome_counter()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome_counter(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)
							print(
							    '''> can_string_be_rearranged_as_palindrome()'''				,					'''\tans ='''				,					can_string_be_rearranged_as_palindrome(A )				,					'''\ttime ='''				,					timeit(
							        '''z.can_string_be_rearranged_as_palindrome(z.check_str)'''				,					setup='''import __main__ as z'''				,					)				,					'''seconds'''				,					)


if __name__ == "__main__":
		__UpperCAmelCase					       =							input(
		    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
		).strip()
		benchmark(check_str)
		__UpperCAmelCase					       =							can_string_be_rearranged_as_palindrome_counter(check_str)
		print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")