

"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a					:str    = logging.get_logger(__name__)




def  _lowercase					(    __lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	DPTConfig(embedding_type="""hybrid"""							)

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	1024
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	4096
            SCREAMING_SNAKE_CASE__   :      str      		=	24
            SCREAMING_SNAKE_CASE__   :      Any      		=	16
            SCREAMING_SNAKE_CASE__   :      str      		=	[5, 11, 17, 23]
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[256, 512, 1024, 1024]
            SCREAMING_SNAKE_CASE__   :      int      		=	(1, 384, 384)

      if "nyu" or "midas" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      Any      		=	768
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	[1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[256, 512, 768, 768]
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	150
            SCREAMING_SNAKE_CASE__   :      Any      		=	16
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	(1, 384, 384)
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	False
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""project"""

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE__   :      str      		=	True
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	768
            SCREAMING_SNAKE_CASE__   :      int      		=	[1, 1, 1, 0.5]
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	150
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	16
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""huggingface/label-files"""
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""ade20k-id2label.json"""
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	json.load(open(cached_download(hf_hub_url(__lowerCAmelCase  ,					__lowerCAmelCase  ,					repo_type="""dataset"""							)							)  ,					"""r"""							)							)
            SCREAMING_SNAKE_CASE__   :      str      		=	{int(__lowerCAmelCase							): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	idalabel
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	[1, 150, 480, 480]

      return config, expected_shape




def  _lowercase					(    __lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(__lowerCAmelCase  ,					__lowerCAmelCase							)




def  _lowercase					(    __lowerCAmelCase							)       ->				Any:
      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            SCREAMING_SNAKE_CASE__   :      Dict      		=	name.replace("""pretrained.model"""  ,					"""dpt.encoder"""							)
      if "pretrained.model" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""pretrained.model"""  ,					"""dpt.embeddings"""							)
      if "patch_embed" in name:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	name.replace("""patch_embed"""  ,					""""""							)
      if "pos_embed" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	name.replace("""pos_embed"""  ,					"""position_embeddings"""							)
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	name.replace("""attn.proj"""  ,					"""attention.output.dense"""							)
      if "proj" in name and "project" not in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""proj"""  ,					"""projection"""							)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	name.replace("""blocks"""  ,					"""layer"""							)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""mlp.fc1"""  ,					"""intermediate.dense"""							)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	name.replace("""mlp.fc2"""  ,					"""output.dense"""							)
      if "norm1" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""norm1"""  ,					"""layernorm_before"""							)
      if "norm2" in name and "backbone" not in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""norm2"""  ,					"""layernorm_after"""							)
      if "scratch.output_conv" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""scratch.output_conv"""  ,					"""head"""							)
      if "scratch" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	name.replace("""scratch"""  ,					"""neck"""							)
      if "layer1_rn" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""layer1_rn"""  ,					"""convs.0"""							)
      if "layer2_rn" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""layer2_rn"""  ,					"""convs.1"""							)
      if "layer3_rn" in name:
            SCREAMING_SNAKE_CASE__   :      Any      		=	name.replace("""layer3_rn"""  ,					"""convs.2"""							)
      if "layer4_rn" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""layer4_rn"""  ,					"""convs.3"""							)
      if "refinenet" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	int(name[len("""neck.refinenet"""							) : len("""neck.refinenet"""							) + 1]							)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace(F'''refinenet{layer_idx}'''  ,					F'''fusion_stage.layers.{abs(layer_idx-4							)}'''							)
      if "out_conv" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""out_conv"""  ,					"""projection"""							)
      if "resConfUnit1" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""resConfUnit1"""  ,					"""residual_layer1"""							)
      if "resConfUnit2" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	name.replace("""resConfUnit2"""  ,					"""residual_layer2"""							)
      if "conv1" in name:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	name.replace("""conv1"""  ,					"""convolution1"""							)
      if "conv2" in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""conv2"""  ,					"""convolution2"""							)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""pretrained.act_postprocess1.0.project.0"""  ,					"""neck.reassemble_stage.readout_projects.0.0"""							)
      if "pretrained.act_postprocess2.0.project.0" in name:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	name.replace("""pretrained.act_postprocess2.0.project.0"""  ,					"""neck.reassemble_stage.readout_projects.1.0"""							)
      if "pretrained.act_postprocess3.0.project.0" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""pretrained.act_postprocess3.0.project.0"""  ,					"""neck.reassemble_stage.readout_projects.2.0"""							)
      if "pretrained.act_postprocess4.0.project.0" in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""pretrained.act_postprocess4.0.project.0"""  ,					"""neck.reassemble_stage.readout_projects.3.0"""							)

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            SCREAMING_SNAKE_CASE__   :      str      		=	name.replace("""pretrained.act_postprocess1.3"""  ,					"""neck.reassemble_stage.layers.0.projection"""							)
      if "pretrained.act_postprocess1.4" in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""pretrained.act_postprocess1.4"""  ,					"""neck.reassemble_stage.layers.0.resize"""							)
      if "pretrained.act_postprocess2.3" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	name.replace("""pretrained.act_postprocess2.3"""  ,					"""neck.reassemble_stage.layers.1.projection"""							)
      if "pretrained.act_postprocess2.4" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""pretrained.act_postprocess2.4"""  ,					"""neck.reassemble_stage.layers.1.resize"""							)
      if "pretrained.act_postprocess3.3" in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""pretrained.act_postprocess3.3"""  ,					"""neck.reassemble_stage.layers.2.projection"""							)
      if "pretrained.act_postprocess4.3" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""pretrained.act_postprocess4.3"""  ,					"""neck.reassemble_stage.layers.3.projection"""							)
      if "pretrained.act_postprocess4.4" in name:
            SCREAMING_SNAKE_CASE__   :      str      		=	name.replace("""pretrained.act_postprocess4.4"""  ,					"""neck.reassemble_stage.layers.3.resize"""							)
      if "pretrained" in name:
            SCREAMING_SNAKE_CASE__   :      Any      		=	name.replace("""pretrained"""  ,					"""dpt"""							)
      if "bn" in name:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	name.replace("""bn"""  ,					"""batch_norm"""							)
      if "head" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""head"""  ,					"""head.head"""							)
      if "encoder.norm" in name:
            SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	name.replace("""encoder.norm"""  ,					"""layernorm"""							)
      if "auxlayer" in name:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	name.replace("""auxlayer"""  ,					"""auxiliary_head.head"""							)
      if "backbone" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""backbone"""  ,					"""backbone.bit.encoder"""							)

      if ".." in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace(""".."""  ,					"""."""							)

      if "stem.conv" in name:
            SCREAMING_SNAKE_CASE__   :      str      		=	name.replace("""stem.conv"""  ,					"""bit.embedder.convolution"""							)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""blocks"""  ,					"""layers"""							)
      if "convolution" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE__   :      List[str]      		=	name.replace("""convolution"""  ,					"""conv"""							)
      if "layer" in name and "backbone" in name:
            SCREAMING_SNAKE_CASE__   :      int      		=	name.replace("""layer"""  ,					"""layers"""							)
      if "backbone.bit.encoder.bit" in name:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	name.replace("""backbone.bit.encoder.bit"""  ,					"""backbone.bit"""							)
      if "embedder.conv" in name:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	name.replace("""embedder.conv"""  ,					"""embedder.convolution"""							)
      if "backbone.bit.encoder.stem.norm" in name:
            SCREAMING_SNAKE_CASE__   :      Dict      		=	name.replace("""backbone.bit.encoder.stem.norm"""  ,					"""backbone.bit.embedder.norm"""							)
      return name




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




def  _lowercase					(    )       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      Any      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      str      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							)
      return im




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      str      		=	get_dpt_config(__lowerCAmelCase							)
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.load(__lowerCAmelCase  ,					map_location="""cpu"""							)
      # remove certain keys
      remove_ignore_keys_(__lowerCAmelCase							)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	state_dict.pop(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Dict      		=	val
      # read in qkv matrices
      read_in_q_k_v(__lowerCAmelCase  ,					__lowerCAmelCase							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__   :      str      		=	DPTForSemanticSegmentation(__lowerCAmelCase							) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase							)
      model.load_state_dict(__lowerCAmelCase							)
      model.eval()

      # Check outputs on an image
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	480 if """ade""" in checkpoint_url else 384
      SCREAMING_SNAKE_CASE__   :      str      		=	DPTImageProcessor(size=__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	prepare_img()
      SCREAMING_SNAKE_CASE__   :      Dict      		=	image_processor(__lowerCAmelCase  ,					return_tensors="""pt"""							)

      # forward pass
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(**__lowerCAmelCase							).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase							).predicted_depth

      if show_prediction:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1							)  ,					size=(image.size[1], image.size[0])  ,					mode="""bicubic"""  ,					align_corners=__lowerCAmelCase  ,					)
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 255							).show()

      if pytorch_dump_folder_path is not None:
            Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
            print(F'''Saving model 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 push_to_hub:
            model.push_to_hub("""ybelkada/dpt-hybrid-midas"""							)
            image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""							)


if __name__ == "__main__":
  a					:str    = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
      type=str,
      help="URL of the original DPT checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default=None,
      type=str,
      required=False,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument(
      "--push_to_hub",
      action="store_true",
  )
  parser.add_argument(
      "--model_name",
      default="dpt-large",
      type=str,
      help="Name of the model, in case you're pushing to the hub.",
  )
  parser.add_argument(
      "--show_prediction",
      action="store_true",
  )

  a					:int    = parser.parse_args()
  convert_dpt_checkpoint(
      args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
  )







"""simple docstring"""


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AlignProcessor, EfficientNetImageProcessor

@require_vision
class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """want""",
               """##want""",
               """##ed""",
               """wa""",
               """un""",
               """runn""",
               """##ing""",
               """,""",
               """low""",
               """lowest""",
           ]
           SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(self.tmpdirname	,    _a  )
           with open(self.image_processor_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 json.dump(_a	,    _a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizer.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           return BertTokenizerFast.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      Any:


           """simple docstring"""


           return EfficientNetImageProcessor.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           shutil.rmtree(self.tmpdirname  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta  )]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[Image.fromarray(np.moveaxis(_a	,    0	,    -1  )  ) for x in image_inputs]
           return image_inputs

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_rust_tokenizer()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_slow.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      str      		=	AlignProcessor.from_pretrained(self.tmpdirname	,    use_fast=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )
           processor_fast.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor.from_pretrained(self.tmpdirname  )

           self.assertEqual(processor_slow.tokenizer.get_vocab()	,    tokenizer_slow.get_vocab()  )
           self.assertEqual(processor_fast.tokenizer.get_vocab()	,    tokenizer_fast.get_vocab()  )
           self.assertEqual(tokenizer_slow.get_vocab()	,    tokenizer_fast.get_vocab()  )
           self.assertIsInstance(processor_slow.tokenizer	,    _a  )
           self.assertIsInstance(processor_fast.tokenizer	,    _a  )

           self.assertEqual(processor_slow.image_processor.to_json_string()	,    image_processor.to_json_string()  )
           self.assertEqual(processor_fast.image_processor.to_json_string()	,    image_processor.to_json_string()  )
           self.assertIsInstance(processor_slow.image_processor	,    _a  )
           self.assertIsInstance(processor_fast.image_processor	,    _a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=self.get_tokenizer()	,    image_processor=self.get_image_processor()  )
           processor.save_pretrained(self.tmpdirname  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_image_processor(do_normalize=_a	,    padding_value=1.0  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor.from_pretrained(
               self.tmpdirname	,    bos_token="""(BOS)"""	,    eos_token="""(EOS)"""	,    do_normalize=_a	,    padding_value=1.0  )

           self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()  )
           self.assertIsInstance(processor.tokenizer	,    _a  )

           self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()  )
           self.assertIsInstance(processor.image_processor	,    _a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_processor(_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(images=_a	,    return_tensors="""np"""  )

           for key in input_image_proc.keys():
                 self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1E-2  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Any      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"""lower newer"""

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	processor(text=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer(_a	,    padding="""max_length"""	,    max_length=64  )

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]  )

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      Any      		=	processor(text=_a	,    images=_a  )

           self.assertListEqual(list(inputs.keys()  )	,    ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]  )

           # test if it raises when no input is passed
           with pytest.raises(_a  ):
                 processor()

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	processor.batch_decode(_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.batch_decode(_a  )

           self.assertListEqual(_a	,    _a  )





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_image_processor()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	AlignProcessor(tokenizer=_a	,    image_processor=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""lower newer"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	processor(text=_a	,    images=_a  )

           self.assertListEqual(list(inputs.keys()  )	,    processor.model_input_names  )




