


'''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_   :				str						      = logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Union[str, Any]=False						,					__A				:    List[Any]=False						,					__A				:    Union[str, Any]=False       )			->	int:

       """simple docstring"""

       a_      :	List[Any]							=						[]
       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  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    Union[str, Any]       )			->	Union[str, Any]:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	str:

       """simple docstring"""

       a_      :	str							=						['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE_						,					SCREAMING_SNAKE_CASE_       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    str						,					__A				:    str       )			->	Any:

       """simple docstring"""

       a_      :	Optional[Any]							=						dct.pop(SCREAMING_SNAKE_CASE_       )
       a_      :	Tuple							=						val







@torch.no_grad()
def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    int       )			->	str:

       """simple docstring"""

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

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

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

       # Define processor
       a_      :	Tuple							=						ViltImageProcessor(size=3_84       )
       a_      :	List[Any]							=						BertTokenizer.from_pretrained('bert-base-uncased'       )
       a_      :	List[Any]							=						ViltProcessor(SCREAMING_SNAKE_CASE_						,					SCREAMING_SNAKE_CASE_       )

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

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

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

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

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


if __name__ == "__main__":
   UpperCAmelCase_   :				List[Any]						      = 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_   :				str						      = parser.parse_args()
   convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from argparse import ArgumentParser

from .env import EnvironmentCommand







def  SCREAMING_SNAKE_CASE_		(    )			->	str:

       """simple docstring"""

       a_      :	str							=						ArgumentParser('Diffusers CLI tool'						,					usage='diffusers-cli <command> [<args>]'       )
       a_      :	Any							=						parser.add_subparsers(help='diffusers-cli command helpers'       )

       # Register commands
       EnvironmentCommand.register_subcommand(__A       )

       # Let's go
       a_      :	int							=						parser.parse_args()

       if not hasattr(__A						,					'func'       ):
              parser.print_help()
              exit(1       )

       # Run
       a_      :	Union[str, Any]							=						args.func(__A       )
       service.run()


if __name__ == "__main__":
   main()


