"""simple docstring"""




import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_a				:		Dict	   =       logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				str:
  print("""Loading config file..."""     )

  def flatten_yaml_as_dict(_lowerCamelCase						:	List[Any]      ,_lowerCamelCase						:	Tuple=""      ,_lowerCamelCase						:	List[Any]="."     ):
    _lowerCAmelCase						:	Union[str, Any]        =					[]
    for k, v in d.items():
      _lowerCAmelCase						:	List[str]        =					parent_key + sep + k if parent_key else k
      if isinstance(_lowerCamelCase      ,collections.abc.MutableMapping     ):
        items.extend(flatten_yaml_as_dict(_lowerCamelCase      ,_lowerCamelCase      ,sep=_lowerCamelCase     ).items()     )
      else:
        items.append((new_key, v)     )
    return dict(_lowerCamelCase     )

  _lowerCAmelCase						:	Optional[Any]        =					argparse.Namespace()
  with open(_lowerCamelCase      ,"""r"""     ) as yaml_file:
    try:
      _lowerCAmelCase						:	int        =					yaml.load(_lowerCamelCase      ,Loader=yaml.FullLoader     )

      _lowerCAmelCase						:	Optional[int]        =					flatten_yaml_as_dict(_lowerCamelCase     )
      for k, v in flat_cfg.items():
        setattr(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )
    except yaml.YAMLError as exc:
      logger.error("""Error while loading config file: {}. Error message: {}""".format(_lowerCamelCase      ,str(_lowerCamelCase     )     )     )
  return config





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Union[str, Any]     )	->				Union[str, Any]:
  _lowerCAmelCase						:	int        =					MobileViTVaConfig()

  _lowerCAmelCase						:	Optional[Any]        =					False

  # dataset
  if task_name.startswith("""imagenet1k_"""     ):
    _lowerCAmelCase						:	Union[str, Any]        =					1000
    if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
      _lowerCAmelCase						:	Any        =					384
    else:
      _lowerCAmelCase						:	Dict        =					256
    _lowerCAmelCase						:	Optional[int]        =					"""imagenet-1k-id2label.json"""
  elif task_name.startswith("""imagenet21k_to_1k_"""     ):
    _lowerCAmelCase						:	Tuple        =					21000
    if int(task_name.strip().split("""_"""     )[-1]     ) == 384:
      _lowerCAmelCase						:	List[str]        =					384
    else:
      _lowerCAmelCase						:	str        =					256
    _lowerCAmelCase						:	Any        =					"""imagenet-22k-id2label.json"""
  elif task_name.startswith("""ade20k_"""     ):
    _lowerCAmelCase						:	Any        =					151
    _lowerCAmelCase						:	Optional[Any]        =					512
    _lowerCAmelCase						:	Tuple        =					"""ade20k-id2label.json"""
    _lowerCAmelCase						:	List[str]        =					True
  elif task_name.startswith("""voc_"""     ):
    _lowerCAmelCase						:	Optional[int]        =					21
    _lowerCAmelCase						:	Union[str, Any]        =					512
    _lowerCAmelCase						:	str        =					"""pascal-voc-id2label.json"""
    _lowerCAmelCase						:	List[Any]        =					True

  # orig_config
  _lowerCAmelCase						:	List[Any]        =					load_orig_config_file(_lowerCamelCase     )
  assert getattr(_lowerCamelCase      ,"""model.classification.name"""      ,-1     ) == "mobilevit_v2", "Invalid model"
  _lowerCAmelCase						:	str        =					getattr(_lowerCamelCase      ,"""model.classification.mitv2.width_multiplier"""      ,1.0     )
  assert (
      getattr(_lowerCamelCase      ,"""model.classification.mitv2.attn_norm_layer"""      ,-1     ) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  _lowerCAmelCase						:	Union[str, Any]        =					getattr(_lowerCamelCase      ,"""model.classification.activation.name"""      ,"""swish"""     )
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    _lowerCAmelCase						:	List[Any]        =					getattr(_lowerCamelCase      ,"""model.segmentation.output_stride"""      ,16     )
    if "_deeplabv3" in task_name:
      _lowerCAmelCase						:	Optional[Any]        =					getattr(_lowerCamelCase      ,"""model.segmentation.deeplabv3.aspp_rates"""      ,[12, 24, 36]     )
      _lowerCAmelCase						:	List[str]        =					getattr(_lowerCamelCase      ,"""model.segmentation.deeplabv3.aspp_out_channels"""      ,512     )
      _lowerCAmelCase						:	Optional[int]        =					getattr(_lowerCamelCase      ,"""model.segmentation.deeplabv3.aspp_dropout"""      ,0.1     )

    # id2label
  _lowerCAmelCase						:	Dict        =					"""huggingface/label-files"""
  _lowerCAmelCase						:	Optional[Any]        =					json.load(open(hf_hub_download(_lowerCamelCase      ,_lowerCamelCase      ,repo_type="""dataset"""     )      ,"""r"""     )     )
  _lowerCAmelCase						:	Optional[Any]        =					{int(_lowerCamelCase     ): v for k, v in idalabel.items()}
  _lowerCAmelCase						:	Optional[Any]        =					idalabel
  _lowerCAmelCase						:	str        =					{v: k for k, v in idalabel.items()}

  return config





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Any     )	->				Optional[Any]:
  _lowerCAmelCase						:	List[Any]        =					dct.pop(_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					val





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Optional[int]=False     )	->				str:
  if base_model:
    _lowerCAmelCase						:	List[Any]        =					""""""
  else:
    _lowerCAmelCase						:	Optional[Any]        =					"""mobilevitv2."""

  _lowerCAmelCase						:	Dict        =					[]
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      _lowerCAmelCase						:	Tuple        =					k[8:]
    else:
      _lowerCAmelCase						:	Any        =					k

    if ".block." in k:
      _lowerCAmelCase						:	Any        =					k_new.replace(""".block."""      ,"""."""     )
    if ".conv." in k:
      _lowerCAmelCase						:	Any        =					k_new.replace(""".conv."""      ,""".convolution."""     )
    if ".norm." in k:
      _lowerCAmelCase						:	List[Any]        =					k_new.replace(""".norm."""      ,""".normalization."""     )

    if "conv_1." in k:
      _lowerCAmelCase						:	Dict        =					k_new.replace("""conv_1."""      ,f"{model_prefix}conv_stem."     )
    for i in [1, 2]:
      if f"layer_{i}." in k:
        _lowerCAmelCase						:	List[Any]        =					k_new.replace(f"layer_{i}."      ,f"{model_prefix}encoder.layer.{i-1}.layer."     )
    if ".exp_1x1." in k:
      _lowerCAmelCase						:	List[Any]        =					k_new.replace(""".exp_1x1."""      ,""".expand_1x1."""     )
    if ".red_1x1." in k:
      _lowerCAmelCase						:	str        =					k_new.replace(""".red_1x1."""      ,""".reduce_1x1."""     )

    for i in [3, 4, 5]:
      if f"layer_{i}.0." in k:
        _lowerCAmelCase						:	List[Any]        =					k_new.replace(f"layer_{i}.0."      ,f"{model_prefix}encoder.layer.{i-1}.downsampling_layer."     )
      if f"layer_{i}.1.local_rep.0." in k:
        _lowerCAmelCase						:	str        =					k_new.replace(f"layer_{i}.1.local_rep.0."      ,f"{model_prefix}encoder.layer.{i-1}.conv_kxk."     )
      if f"layer_{i}.1.local_rep.1." in k:
        _lowerCAmelCase						:	Dict        =					k_new.replace(f"layer_{i}.1.local_rep.1."      ,f"{model_prefix}encoder.layer.{i-1}.conv_1x1."     )

    for i in [3, 4, 5]:
      if i == 3:
        _lowerCAmelCase						:	Any        =					[0, 1]
      elif i == 4:
        _lowerCAmelCase						:	Dict        =					[0, 1, 2, 3]
      elif i == 5:
        _lowerCAmelCase						:	Optional[Any]        =					[0, 1, 2]

      for j in j_in:
        if f"layer_{i}.1.global_rep.{j}." in k:
          _lowerCAmelCase						:	List[Any]        =					k_new.replace(
              f"layer_{i}.1.global_rep.{j}."      ,f"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."     )
      if f"layer_{i}.1.global_rep.{j+1}." in k:
        _lowerCAmelCase						:	Union[str, Any]        =					k_new.replace(
            f"layer_{i}.1.global_rep.{j+1}."      ,f"{model_prefix}encoder.layer.{i-1}.layernorm."     )

      if f"layer_{i}.1.conv_proj." in k:
        _lowerCAmelCase						:	Any        =					k_new.replace(f"layer_{i}.1.conv_proj."      ,f"{model_prefix}encoder.layer.{i-1}.conv_projection."     )

    if "pre_norm_attn.0." in k:
      _lowerCAmelCase						:	int        =					k_new.replace("""pre_norm_attn.0."""      ,"""layernorm_before."""     )
    if "pre_norm_attn.1." in k:
      _lowerCAmelCase						:	int        =					k_new.replace("""pre_norm_attn.1."""      ,"""attention."""     )
    if "pre_norm_ffn.0." in k:
      _lowerCAmelCase						:	int        =					k_new.replace("""pre_norm_ffn.0."""      ,"""layernorm_after."""     )
    if "pre_norm_ffn.1." in k:
      _lowerCAmelCase						:	Optional[Any]        =					k_new.replace("""pre_norm_ffn.1."""      ,"""ffn.conv1."""     )
    if "pre_norm_ffn.3." in k:
      _lowerCAmelCase						:	Any        =					k_new.replace("""pre_norm_ffn.3."""      ,"""ffn.conv2."""     )

    if "classifier.1." in k:
      _lowerCAmelCase						:	Optional[Any]        =					k_new.replace("""classifier.1."""      ,"""classifier."""     )

    if "seg_head." in k:
      _lowerCAmelCase						:	List[Any]        =					k_new.replace("""seg_head."""      ,"""segmentation_head."""     )
    if ".aspp_layer." in k:
      _lowerCAmelCase						:	int        =					k_new.replace(""".aspp_layer."""      ,"""."""     )
    if ".aspp_pool." in k:
      _lowerCAmelCase						:	Dict        =					k_new.replace(""".aspp_pool."""      ,"""."""     )

    rename_keys.append((k, k_new)     )
  return rename_keys





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str     )	->				Tuple:
  _lowerCAmelCase						:	List[str]        =					[]
  for k in state_dict.keys():
    if k.startswith("""seg_head.aux_head."""     ):
      keys_to_ignore.append(_lowerCamelCase     )
  for k in keys_to_ignore:
    state_dict.pop(_lowerCamelCase      ,_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      )	->				List[str]:
  _lowerCAmelCase						:	List[Any]        =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  _lowerCAmelCase						:	List[Any]        =					Image.open(requests.get(_lowerCamelCase      ,stream=_lowerCamelCase     ).raw     )
  return im





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Dict      ,_lowerCamelCase						:	Any     )	->				Optional[Any]:
  _lowerCAmelCase						:	List[Any]        =					get_mobilevitva_config(_lowerCamelCase      ,_lowerCamelCase     )

  # load original state_dict
  _lowerCAmelCase						:	Tuple        =					torch.load(_lowerCamelCase      ,map_location="""cpu"""     )

  # load huggingface model
  if task_name.startswith("""ade20k_"""     ) or task_name.startswith("""voc_"""     ):
    _lowerCAmelCase						:	Optional[int]        =					MobileViTVaForSemanticSegmentation(_lowerCamelCase     ).eval()
    _lowerCAmelCase						:	int        =					False
  else:
    _lowerCAmelCase						:	Any        =					MobileViTVaForImageClassification(_lowerCamelCase     ).eval()
    _lowerCAmelCase						:	Any        =					False

  # remove and rename some keys of load the original model
  _lowerCAmelCase						:	List[Any]        =					checkpoint
  remove_unused_keys(_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					create_rename_keys(_lowerCamelCase      ,base_model=_lowerCamelCase     )
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase     )

  # load modified state_dict
  model.load_state_dict(_lowerCamelCase     )

  # Check outputs on an image, prepared by MobileViTImageProcessor
  _lowerCAmelCase						:	str        =					MobileViTImageProcessor(crop_size=config.image_size      ,size=config.image_size + 32     )
  _lowerCAmelCase						:	Union[str, Any]        =					image_processor(images=prepare_img()      ,return_tensors="""pt"""     )
  _lowerCAmelCase						:	Tuple        =					model(**_lowerCamelCase     )

  # verify classification model
  if task_name.startswith("""imagenet"""     ):
    _lowerCAmelCase						:	List[str]        =					outputs.logits
    _lowerCAmelCase						:	List[Any]        =					logits.argmax(-1     ).item()
    print("""Predicted class:"""      ,model.config.idalabel[predicted_class_idx]     )
    if task_name.startswith("""imagenet1k_256"""     ) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      _lowerCAmelCase						:	Tuple        =					torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]     )
      assert torch.allclose(logits[0, :3]      ,_lowerCamelCase      ,atol=1e-4     )

  Path(_lowerCamelCase     ).mkdir(exist_ok=_lowerCamelCase     )
  print(f"Saving model {task_name} to {pytorch_dump_folder_path}"     )
  model.save_pretrained(_lowerCamelCase     )
  print(f"Saving image processor to {pytorch_dump_folder_path}"     )
  image_processor.save_pretrained(_lowerCamelCase     )


if __name__ == "__main__":
     _a				:		List[str]	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--task',
         default='imagenet1k_256',
         type=str,
         help=(
             'Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '
             '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
         ),
         choices=[
             'imagenet1k_256',
             'imagenet1k_384',
             'imagenet21k_to_1k_256',
             'imagenet21k_to_1k_384',
             'ade20k_deeplabv3',
             'voc_deeplabv3',
         ],
     )

     parser.add_argument(
         '--orig_checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
     )
     parser.add_argument('--orig_config_path', required=True, type=str, help='Path to the original config file.')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
     )

     _a				:		List[Any]	   =       parser.parse_args()
     convert_mobilevitva_checkpoint(
         args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
     )





"""simple docstring"""




import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         LayoutLMvaForQuestionAnswering,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaModel,
     )
     from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor
class 						__A							:






     def __init__(		self    ,					a__    ,					a__=2    ,					a__=3    ,					a__=4    ,					a__=2    ,					a__=7    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=99    ,					a__=36    ,					a__=3    ,					a__=4    ,					a__=37    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=16    ,					a__=2    ,					a__=0.0_2    ,					a__=6    ,					a__=6    ,					a__=3    ,					a__=4    ,					a__=None    ,					a__=1000    ,					):
       _lowerCAmelCase						:	Union[str, Any]        =					parent
       _lowerCAmelCase						:	Optional[Any]        =					batch_size
       _lowerCAmelCase						:	List[Any]        =					num_channels
       _lowerCAmelCase						:	str        =					image_size
       _lowerCAmelCase						:	str        =					patch_size
       _lowerCAmelCase						:	str        =					text_seq_length
       _lowerCAmelCase						:	List[Any]        =					is_training
       _lowerCAmelCase						:	List[str]        =					use_input_mask
       _lowerCAmelCase						:	List[Any]        =					use_token_type_ids
       _lowerCAmelCase						:	Optional[Any]        =					use_labels
       _lowerCAmelCase						:	Any        =					vocab_size
       _lowerCAmelCase						:	str        =					hidden_size
       _lowerCAmelCase						:	Dict        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	str        =					intermediate_size
       _lowerCAmelCase						:	str        =					hidden_act
       _lowerCAmelCase						:	Optional[Any]        =					hidden_dropout_prob
       _lowerCAmelCase						:	Any        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Tuple        =					max_position_embeddings
       _lowerCAmelCase						:	Dict        =					type_vocab_size
       _lowerCAmelCase						:	Tuple        =					type_sequence_label_size
       _lowerCAmelCase						:	Union[str, Any]        =					initializer_range
       _lowerCAmelCase						:	Dict        =					coordinate_size
       _lowerCAmelCase						:	Optional[int]        =					shape_size
       _lowerCAmelCase						:	str        =					num_labels
       _lowerCAmelCase						:	Optional[Any]        =					num_choices
       _lowerCAmelCase						:	str        =					scope
       _lowerCAmelCase						:	Dict        =					range_bbox

       # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
       _lowerCAmelCase						:	Optional[int]        =					text_seq_length
       _lowerCAmelCase						:	Any        =					(image_size // patch_size) ** 2 + 1
       _lowerCAmelCase						:	Any        =					self.text_seq_length + self.image_seq_length






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.vocab_size  )

       _lowerCAmelCase						:	Tuple        =					ids_tensor([self.batch_size, self.text_seq_length, 4]    ,					self.range_bbox  )
       # Ensure that bbox is legal
       for i in range(bbox.shape[0]  ):
         for j in range(bbox.shape[1]  ):
           if bbox[i, j, 3] < bbox[i, j, 1]:
             _lowerCAmelCase						:	Optional[Any]        =					bbox[i, j, 3]
             _lowerCAmelCase						:	List[str]        =					bbox[i, j, 1]
             _lowerCAmelCase						:	List[Any]        =					t
           if bbox[i, j, 2] < bbox[i, j, 0]:
             _lowerCAmelCase						:	int        =					bbox[i, j, 2]
             _lowerCAmelCase						:	Optional[int]        =					bbox[i, j, 0]
             _lowerCAmelCase						:	Optional[int]        =					t

       _lowerCAmelCase						:	Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       _lowerCAmelCase						:	List[str]        =					None
       if self.use_input_mask:
         _lowerCAmelCase						:	int        =					random_attention_mask([self.batch_size, self.text_seq_length]  )

       _lowerCAmelCase						:	str        =					None
       if self.use_token_type_ids:
         _lowerCAmelCase						:	Tuple        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.type_vocab_size  )

       _lowerCAmelCase						:	int        =					None
       _lowerCAmelCase						:	int        =					None
       if self.use_labels:
         _lowerCAmelCase						:	List[str]        =					ids_tensor([self.batch_size]    ,					self.type_sequence_label_size  )
         _lowerCAmelCase						:	str        =					ids_tensor([self.batch_size, self.text_seq_length]    ,					self.num_labels  )

       _lowerCAmelCase						:	str        =					LayoutLMvaConfig(
           vocab_size=self.vocab_size    ,					hidden_size=self.hidden_size    ,					num_hidden_layers=self.num_hidden_layers    ,					num_attention_heads=self.num_attention_heads    ,					intermediate_size=self.intermediate_size    ,					hidden_act=self.hidden_act    ,					hidden_dropout_prob=self.hidden_dropout_prob    ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,					max_position_embeddings=self.max_position_embeddings    ,					type_vocab_size=self.type_vocab_size    ,					initializer_range=self.initializer_range    ,					coordinate_size=self.coordinate_size    ,					shape_size=self.shape_size    ,					input_size=self.image_size    ,					patch_size=self.patch_size    ,					)

       return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					LayoutLMvaModel(config=a__  )
       model.to(a__  )
       model.eval()

       # text + image
       _lowerCAmelCase						:	Optional[Any]        =					model(a__    ,					pixel_values=a__  )
       _lowerCAmelCase						:	Any        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	List[Any]        =					model(a__    ,					bbox=a__    ,					pixel_values=a__    ,					token_type_ids=a__  )
       _lowerCAmelCase						:	Tuple        =					model(a__    ,					bbox=a__    ,					pixel_values=a__  )

       self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)  )

       # text only
       _lowerCAmelCase						:	Dict        =					model(a__  )
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.text_seq_length, self.hidden_size)  )

       # image only
       _lowerCAmelCase						:	Optional[Any]        =					model(pixel_values=a__  )
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,					(self.batch_size, self.image_seq_length, self.hidden_size)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	int        =					self.num_labels
       _lowerCAmelCase						:	int        =					LayoutLMvaForSequenceClassification(a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Tuple        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__    ,					)
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.num_labels
       _lowerCAmelCase						:	str        =					LayoutLMvaForTokenClassification(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	List[str]        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					labels=a__    ,					)
       self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.text_seq_length, self.num_labels)  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					LayoutLMvaForQuestionAnswering(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	str        =					model(
           a__    ,					bbox=a__    ,					pixel_values=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					start_positions=a__    ,					end_positions=a__    ,					)
       self.parent.assertEqual(result.start_logits.shape    ,					(self.batch_size, self.seq_length)  )
       self.parent.assertEqual(result.end_logits.shape    ,					(self.batch_size, self.seq_length)  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     (
           _lowerCAmelCase
       )      ,     
       )						:	Any        =					config_and_inputs
       _lowerCAmelCase						:	Union[str, Any]        =					{
           """input_ids""": input_ids,
           """bbox""": bbox,
           """pixel_values""": pixel_values,
           """token_type_ids""": token_type_ids,
           """attention_mask""": input_mask,
       }
       return config, inputs_dict







@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Tuple		 =		False
     _UpperCamelCase							:      Any		 =		False
     _UpperCamelCase							:      Any		 =		False

     _UpperCamelCase							:      int		 =		(
         (
             LayoutLMvaModel,
             LayoutLMvaForSequenceClassification,
             LayoutLMvaForTokenClassification,
             LayoutLMvaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:      Union[str, Any]		 =		(
         {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
         if is_torch_available()
         else {}
     )






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
       # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
       # the sequence dimension of the text embedding only.
       # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
       return True






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					LayoutLMvaModelTester(self  )
       _lowerCAmelCase						:	int        =					ConfigTester(self    ,					config_class=a__    ,					hidden_size=37  )






     def    __A  (		self    ,					a__    ,					a__    ,					a__=False  ):
       _lowerCAmelCase						:	List[str]        =					copy.deepcopy(a__  )
       if model_class in get_values(a__  ):
         _lowerCAmelCase						:	Optional[int]        =					{
             k: v.unsqueeze(1  ).expand(-1    ,					self.model_tester.num_choices    ,					-1  ).contiguous()
             if isinstance(a__    ,					torch.Tensor  ) and v.ndim > 1
             else v
             for k, v in inputs_dict.items()
         }
       if return_labels:
         if model_class in get_values(a__  ):
           _lowerCAmelCase						:	List[Any]        =					torch.ones(self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in get_values(a__  ):
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
           _lowerCAmelCase						:	Any        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	Optional[int]        =					torch.zeros(
               self.model_tester.batch_size    ,					dtype=torch.long    ,					device=a__  )
         elif model_class in [
             *get_values(a__  ),
         ]:
           _lowerCAmelCase						:	int        =					torch.zeros(
               (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,					dtype=torch.long    ,					device=a__    ,					)

       return inputs_dict






     def    __A  (		self  ):
       self.config_tester.run_common_tests()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         _lowerCAmelCase						:	int        =					type
         self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a__  )






     @slow
     def    __A  (		self  ):
       for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase						:	str        =					LayoutLMvaModel.from_pretrained(a__  )
         self.assertIsNotNone(a__  )







def 							SCREAMING_SNAKE_CASE					(      )	->				str:
  _lowerCAmelCase						:	Any        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image






@require_torch
class 						__A							(			unittest.TestCase		):






     @cached_property
     def    __A  (		self  ):
       return LayoutLMvaImageProcessor(apply_ocr=a__  ) if is_vision_available() else None






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""  ).to(a__  )

       _lowerCAmelCase						:	Dict        =					self.default_image_processor
       _lowerCAmelCase						:	Optional[int]        =					prepare_img()
       _lowerCAmelCase						:	Dict        =					image_processor(images=a__    ,					return_tensors="""pt"""  ).pixel_values.to(a__  )

       _lowerCAmelCase						:	Optional[Any]        =					torch.tensor([[1, 2]]  )
       _lowerCAmelCase						:	Dict        =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

       # forward pass
       _lowerCAmelCase						:	str        =					model(
           input_ids=input_ids.to(a__  )    ,					bbox=bbox.to(a__  )    ,					pixel_values=pixel_values.to(a__  )    ,					)

       # verify the logits
       _lowerCAmelCase						:	Optional[int]        =					torch.Size((1, 199, 768)  )
       self.assertEqual(outputs.last_hidden_state.shape    ,					a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					torch.tensor(
           [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]  ).to(a__  )

       self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,					a__    ,					atol=1e-4  )  )




