'''simple docstring'''




import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)

def      UpperCamelCase_							( _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				Any=False      ,     _UpperCAmelCase  :				List[Any]=False      ,     _UpperCAmelCase  :				Union[str, Any]="data2vec."   )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   "backbone." if is_semantic else ""

       _UpperCAmelCase					:  Optional[int]											=   []
       for i in range(config.num_hidden_layers   ):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.norm1.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.norm2.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")   )
              rename_keys.append(
                  (F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")   )
              rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")   )

       # projection layer + position embeddings
       rename_keys.extend(
           [
               (F"""{prefix}cls_token""", F"""{hf_prefix}embeddings.cls_token"""),
               (F"""{prefix}patch_embed.proj.weight""", F"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
               (F"""{prefix}patch_embed.proj.bias""", F"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
           ]   )

       if has_lm_head:
              # mask token + shared relative position bias + layernorm
              rename_keys.extend(
                  [
                      ("mask_token", F"""{hf_prefix}embeddings.mask_token"""),
                      (
                          "rel_pos_bias.relative_position_bias_table",
                          F"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
                      ),
                      (
                          "rel_pos_bias.relative_position_index",
                          F"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
                      ),
                      ("norm.weight", "layernorm.weight"),
                      ("norm.bias", "layernorm.bias"),
                  ]   )
       elif is_semantic:
              # semantic segmentation classification heads
              rename_keys.extend(
                  [
                      ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
                      ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
                      ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
                      ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
                  ]   )
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ("fc_norm.weight", F"""{hf_prefix}pooler.layernorm.weight"""),
                      ("fc_norm.bias", F"""{hf_prefix}pooler.layernorm.bias"""),
                      ("head.weight", "classifier.weight"),
                      ("head.bias", "classifier.bias"),
                  ]   )

       return rename_keys

def      UpperCamelCase_							( _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				List[str]=False      ,     _UpperCAmelCase  :				List[Any]=False      ,     _UpperCAmelCase  :				Union[str, Any]="data2vec_vision."   )   ->   str:


       """simple docstring"""







       for i in range(config.num_hidden_layers   ):
              _UpperCAmelCase					:  Union[str, Any]											=   "backbone." if is_semantic else ""
              # queries, keys and values
              _UpperCAmelCase					:  Union[str, Any]											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""   )
              _UpperCAmelCase					:  Dict											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""   )
              _UpperCAmelCase					:  int											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""   )

              _UpperCAmelCase					:  str											=   in_proj_weight[
                  : config.hidden_size, :
              ]
              _UpperCAmelCase					:  Optional[Any]											=   q_bias
              _UpperCAmelCase					:  List[str]											=   in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              _UpperCAmelCase					:  List[Any]											=   in_proj_weight[
                  -config.hidden_size :, :
              ]
              _UpperCAmelCase					:  Any											=   v_bias

              # gamma_1 and gamma_2
              # we call them lambda because otherwise they are renamed when using .from_pretrained
              _UpperCAmelCase					:  List[str]											=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""   )
              _UpperCAmelCase					:  Optional[Any]											=   state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""   )

              _UpperCAmelCase					:  Dict											=   gamma_a
              _UpperCAmelCase					:  Dict											=   gamma_a

              # relative_position bias table + index
              if not has_lm_head:
                     # each layer has its own relative position bias
                     _UpperCAmelCase					:  Union[str, Any]											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""   )
                     _UpperCAmelCase					:  Any											=   state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""   )

                     _UpperCAmelCase					:  Union[str, Any]											=   table
                     _UpperCAmelCase					:  List[str]											=   index

def      UpperCamelCase_							( )   ->   Optional[int]:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   argparse.ArgumentParser(
           "Convert Data2VecVision to HF for image classification and pretraining"      ,     add_help=snake_case__   )
       parser.add_argument("--hf_checkpoint_name"      ,     type=snake_case__   )
       parser.add_argument("--input_size"      ,     default=224      ,     type=snake_case__      ,     help="images input size"   )
       parser.add_argument("--beit_checkpoint"      ,     default=""      ,     help="beit checkpoint"   )

       return parser.parse_args()

def      UpperCamelCase_							( _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				str   )   ->   int:


       """simple docstring"""







       def load_state_dict(_UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				Tuple=""      ,     _UpperCAmelCase  :				Tuple="relative_position_index"   ):
              _UpperCAmelCase					:  str											=   []
              _UpperCAmelCase					:  Optional[int]											=   []
              _UpperCAmelCase					:  Optional[Any]											=   []
              # copy state_dict so _load_from_state_dict can modify it
              _UpperCAmelCase					:  int											=   getattr(snake_case__      ,     "_metadata"      ,     snake_case__   )
              _UpperCAmelCase					:  Optional[Any]											=   state_dict.copy()
              if metadata is not None:
                     _UpperCAmelCase					:  Optional[int]											=   metadata

              def load(_UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				str=""   ):
                     _UpperCAmelCase					:  Optional[int]											=   {} if metadata is None else metadata.get(prefix[:-1]      ,     {}   )
                     module._load_from_state_dict(
                         snake_case__      ,     snake_case__      ,     snake_case__      ,     snake_case__      ,     snake_case__      ,     snake_case__      ,     snake_case__   )
                     for name, child in module._modules.items():
                            if child is not None:
                                   load(snake_case__      ,     prefix + name + "."   )

              load(snake_case__      ,     prefix=snake_case__   )

              _UpperCAmelCase					:  Optional[int]											=   []
              _UpperCAmelCase					:  Optional[Any]											=   []
              for key in missing_keys:
                     _UpperCAmelCase					:  Dict											=   True
                     for ignore_key in ignore_missing.split("|"   ):
                            if ignore_key in key:
                                   _UpperCAmelCase					:  Optional[int]											=   False
                                   break
                     if keep_flag:
                            warn_missing_keys.append(snake_case__   )
                     else:
                            ignore_missing_keys.append(snake_case__   )

              _UpperCAmelCase					:  Optional[int]											=   warn_missing_keys

              if len(snake_case__   ) > 0:
                     print(
                         "Weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__      ,     snake_case__   )   )
              if len(snake_case__   ) > 0:
                     print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__      ,     snake_case__   )   )
              if len(snake_case__   ) > 0:
                     print(
                         "Ignored weights of {} not initialized from pretrained model: {}".format(
                             model.__class__.__name__      ,     snake_case__   )   )
              if len(snake_case__   ) > 0:
                     print("\n".join(snake_case__   )   )

       _UpperCAmelCase					:  List[str]											=   {
           "pretrained": False,
           "use_shared_rel_pos_bias": True,
           "use_abs_pos_emb": False,
           "init_values": 0.1,
       }

       if is_finetuned:
              model_kwargs.update(
                  {
                      "num_classes": 1_000,
                      "use_mean_pooling": True,
                      "init_scale": 0.0_0_1,
                      "use_rel_pos_bias": True,
                  }   )

       _UpperCAmelCase					:  Dict											=   create_model(
           "beit_large_patch16_224" if is_large else "beit_base_patch16_224"      ,     **snake_case__      ,     )
       _UpperCAmelCase					:  Optional[int]											=   model.patch_embed.patch_size
       _UpperCAmelCase					:  List[Any]											=   (args.input_size // patch_size[0], args.input_size // patch_size[1])
       _UpperCAmelCase					:  Union[str, Any]											=   torch.load(args.beit_checkpoint      ,     map_location="cpu"   )

       print(F"""Load ckpt from {args.beit_checkpoint}"""   )
       _UpperCAmelCase					:  Optional[Any]											=   None
       for model_key in ("model", "module"):
              if model_key in checkpoint:
                     _UpperCAmelCase					:  int											=   checkpoint[model_key]
                     print(F"""Load state_dict by model_key = {model_key}"""   )
                     break

       _UpperCAmelCase					:  Any											=   list(checkpoint_model.keys()   )
       for key in all_keys:
              if "relative_position_index" in key:
                     checkpoint_model.pop(snake_case__   )

              if "relative_position_bias_table" in key:
                     _UpperCAmelCase					:  List[Any]											=   checkpoint_model[key]
                     _UpperCAmelCase					:  List[Any]											=   rel_pos_bias.size()
                     _UpperCAmelCase					:  Optional[Any]											=   model.state_dict()[key].size()
                     _UpperCAmelCase					:  Optional[int]											=   model.patch_embed.patch_shape
                     if dst_patch_shape[0] != dst_patch_shape[1]:
                            raise NotImplementedError()

       load_state_dict(snake_case__      ,     snake_case__      ,     prefix=""   )

       return model

def      UpperCamelCase_							( )   ->   Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase					:  Any											=   get_args()

       _UpperCAmelCase					:  str											=   "ft1k" in args.hf_checkpoint_name
       _UpperCAmelCase					:  Tuple											=   "large" in args.hf_checkpoint_name

       if is_finetuned:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
              # into this folder.
              import modeling_finetune  # noqa: F401
       else:
              # To convert Beit's data2vec_vision to HF you need to copy
              # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
              # into this folder
              # IMPORTANT: Note that for now we've only converted the down-stream
              # model and not the full pretrained model. This means for the integration
              # test you need to add a `return x` after the following line:
              # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
              # to make the integration test pass.
              import modeling_cyclical  # noqa: F401

       # 1. Create model config
       _UpperCAmelCase					:  List[Any]											=   DataaVecVisionConfig()
       if is_finetuned:
              _UpperCAmelCase					:  int											=   True
              _UpperCAmelCase					:  Any											=   False
              _UpperCAmelCase					:  Dict											=   True
              _UpperCAmelCase					:  Optional[int]											=   1_000

              _UpperCAmelCase					:  Optional[Any]											=   "huggingface/label-files"
              _UpperCAmelCase					:  Union[str, Any]											=   "imagenet-1k-id2label.json"
              _UpperCAmelCase					:  Union[str, Any]											=   json.load(open(hf_hub_download(snake_case__      ,     snake_case__      ,     repo_type="dataset"   )      ,     "r"   )   )
              _UpperCAmelCase					:  List[str]											=   {int(snake_case__   ): v for k, v in idalabel.items()}
              _UpperCAmelCase					:  Tuple											=   idalabel
              _UpperCAmelCase					:  Dict											=   {v: k for k, v in idalabel.items()}
       else:
              _UpperCAmelCase					:  int											=   False
              _UpperCAmelCase					:  int											=   True
              _UpperCAmelCase					:  str											=   False

       if is_large:
              _UpperCAmelCase					:  Tuple											=   1_024
              _UpperCAmelCase					:  str											=   4_096
              _UpperCAmelCase					:  Optional[int]											=   24
              _UpperCAmelCase					:  Any											=   16

       # 2. Load Beit model
       _UpperCAmelCase					:  Dict											=   load_beit_model(snake_case__      ,     snake_case__      ,     snake_case__   )
       orig_model.eval()

       # 3. Forward Beit model
       _UpperCAmelCase					:  Dict											=   BeitImageProcessor(size=config.image_size      ,     do_center_crop=snake_case__   )
       _UpperCAmelCase					:  Tuple											=   Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"   )
       _UpperCAmelCase					:  List[str]											=   image_processor(images=snake_case__      ,     return_tensors="pt"   )
       _UpperCAmelCase					:  List[str]											=   encoding["pixel_values"]

       _UpperCAmelCase					:  str											=   (pixel_values,) if is_finetuned else (pixel_values, None)
       with torch.no_grad():
              _UpperCAmelCase					:  int											=   orig_model(*snake_case__   )

       # 4. Load HF Data2VecVision model
       if is_finetuned:
              _UpperCAmelCase					:  Tuple											=   DataaVecVisionForImageClassification(snake_case__   )
              hf_model.eval()
              _UpperCAmelCase					:  List[Any]											=   False
              _UpperCAmelCase					:  List[str]											=   "data2vec_vision."
       else:
              _UpperCAmelCase					:  List[Any]											=   DataaVecVisionModel(snake_case__   )
              hf_model.eval()
              _UpperCAmelCase					:  List[Any]											=   True
              _UpperCAmelCase					:  int											=   ""

       _UpperCAmelCase					:  int											=   create_rename_keys(snake_case__      ,     hf_prefix=snake_case__      ,     has_lm_head=snake_case__   )
       _UpperCAmelCase					:  Any											=   orig_model.state_dict()
       for src, dest in rename_keys:
              _UpperCAmelCase					:  List[Any]											=   state_dict.pop(snake_case__   )
              _UpperCAmelCase					:  List[str]											=   val

       read_in_q_k_v(snake_case__      ,     snake_case__      ,     hf_prefix=snake_case__      ,     has_lm_head=snake_case__   )
       _UpperCAmelCase					:  str											=   hf_model.load_state_dict(snake_case__      ,     strict=snake_case__   )
       print("HF missing"      ,     snake_case__   )
       print("HF unexpected_keys"      ,     snake_case__   )

       # 5. Forward HF Data2VecVision model
       with torch.no_grad():
              _UpperCAmelCase					:  List[Any]											=   hf_model(snake_case__   )

       _UpperCAmelCase					:  Any											=   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

       # 6. Compare
       _UpperCAmelCase					:  List[Any]											=   torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

       print(F"""max_absolute_diff = {max_absolute_diff}"""   )
       _UpperCAmelCase					:  List[Any]											=   torch.allclose(snake_case__      ,     snake_case__      ,     atol=1e-3   )
       print("Do both models output the same tensors?"      ,     "🔥" if success else "💩"   )
       if not success:
              raise Exception("Something went wRoNg"   )

       # 7. Save
       print(F"""Saving to {args.hf_checkpoint_name}"""   )
       hf_model.save_pretrained(args.hf_checkpoint_name   )
       image_processor.save_pretrained(args.hf_checkpoint_name   )


if __name__ == "__main__":
  main()
  # Run the following to convert checkpoints
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_base.pt \
  #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./pretrained_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large"
  #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
  #          --beit_checkpoint ./finetuned_large.pt \
  #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"


'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

             _UpperCAmelCase					:  Optional[Any]											=   [Image.fromarray(np.moveaxis(A						, 0						, -1	)	) for x in image_inputs]

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.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						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, return_tensors="np"	)

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




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

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




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

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

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




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

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

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

