




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







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					['head.weight', 'head.bias']
				for k in ignore_keys:
								state_dict.pop(lowercase__							,					lowercase__	)







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Any					=					dct.pop(lowercase__	)
				_lowerCamelCase  :    Tuple					=					val







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

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

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

				# Define processor
				_lowerCamelCase  :    Optional[int]					=					ViltImageProcessor(size=384	)
				_lowerCamelCase  :    int					=					BertTokenizer.from_pretrained('bert-base-uncased'	)
				_lowerCamelCase  :    Optional[Any]					=					ViltProcessor(lowercase__							,					lowercase__	)

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

				# Verify outputs
				if mlm_model:
								_lowerCamelCase  :    Union[str, Any]					=					torch.Size([1, 11, 30522]	)
								_lowerCamelCase  :    Optional[Any]					=					torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]	)
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]							,					lowercase__							,					atol=1E-4	)

								# verify masked token prediction equals "cats"
								_lowerCamelCase  :    List[str]					=					outputs.logits[0, 4, :].argmax(-1	).item()
								assert tokenizer.decode([predicted_id]	) == "cats"
				elif vqa_model:
								_lowerCamelCase  :    List[Any]					=					torch.Size([1, 3129]	)
								_lowerCamelCase  :    List[Any]					=					torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]	)
								assert torch.allclose(outputs.logits[0, :3]							,					lowercase__							,					atol=1E-4	)
								assert outputs.logits.shape == expected_shape
								assert torch.allclose(outputs.logits[0, 0, :3]							,					lowercase__							,					atol=1E-4	)

								# verify vqa prediction equals "2"
								_lowerCamelCase  :    Tuple					=					outputs.logits.argmax(-1	).item()
								assert model.config.idalabel[predicted_idx] == "2"
				elif nlvr_model:
								_lowerCamelCase  :    Union[str, Any]					=					torch.Size([1, 2]	)
								_lowerCamelCase  :    int					=					torch.tensor([-2.8_7_2_1, 2.1_2_9_1]	)
								assert torch.allclose(outputs.logits[0, :3]							,					lowercase__							,					atol=1E-4	)
								assert outputs.logits.shape == expected_shape

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


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 Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """umt5"""
			lowerCamelCase__   =    ["""past_key_values"""]
			def __init__(						self						,   lowercase=250112						,   lowercase=512						,   lowercase=64						,   lowercase=1024						,   lowercase=8						,   lowercase=None						,   lowercase=6						,   lowercase=32						,   lowercase=128						,   lowercase=0.1						,   lowercase=1E-6						,   lowercase=1.0						,   lowercase="gated-gelu"						,   lowercase=True						,   lowercase=True						,   lowercase="T5Tokenizer"						,   lowercase=True						,   lowercase=0						,   lowercase=1						,   lowercase=0						,   **lowercase						,   ):
							super().__init__(
							    is_encoder_decoder=lowercase						,   tokenizer_class=lowercase						,   tie_word_embeddings=lowercase						,   pad_token_id=lowercase						,   eos_token_id=lowercase						,   decoder_start_token_id=lowercase						,   **lowercase						,   )
							_lowerCamelCase  :    Union[str, Any]					=					vocab_size
							_lowerCamelCase  :    Optional[Any]					=					d_model
							_lowerCamelCase  :    Optional[Any]					=					d_kv
							_lowerCamelCase  :    Optional[int]					=					d_ff
							_lowerCamelCase  :    List[str]					=					num_layers
							_lowerCamelCase  :    Union[str, Any]					=					(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							_lowerCamelCase  :    List[str]					=					num_heads
							_lowerCamelCase  :    int					=					relative_attention_num_buckets
							_lowerCamelCase  :    Union[str, Any]					=					relative_attention_max_distance
							_lowerCamelCase  :    int					=					dropout_rate
							_lowerCamelCase  :    List[Any]					=					layer_norm_epsilon
							_lowerCamelCase  :    Union[str, Any]					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					feed_forward_proj
							_lowerCamelCase  :    Dict					=					use_cache

							_lowerCamelCase  :    Dict					=					self.feed_forward_proj.split('-'						)
							_lowerCamelCase  :    List[str]					=					act_info[-1]
							_lowerCamelCase  :    int					=					act_info[0] == 'gated'

							if len(lowercase						) > 1 and act_info[0] != "gated" or len(lowercase						) > 2:
											raise ValueError(
											    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
											    '\'gated-gelu\' or \'relu\''						)

							if feed_forward_proj == "gated-gelu":
											_lowerCamelCase  :    Optional[int]					=					'gelu_new'
			@property
			def       A_					(						self						):
							return self.d_model
			@property
			def       A_					(						self						):
							return self.num_heads






			@property
			def       A_					(						self						):
							return self.num_layers






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
											_lowerCamelCase  :    Optional[int]					=					'past_encoder_sequence + sequence'
											_lowerCamelCase  :    int					=					{0: 'batch'}
											_lowerCamelCase  :    List[str]					=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
											_lowerCamelCase  :    Dict					=					{0: 'batch', 1: 'decoder_sequence'}
											_lowerCamelCase  :    str					=					{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
											self.fill_with_past_key_values_(lowercase						,   direction='inputs'						)

							return common_inputs
			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def       A_					(						self						):
							return 13






			@property
			def       A_					(						self						):
							return 5E-4