






"""simple docstring"""



import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_UpperCamelCase						:    Union[str, Any]		=						{
    'vit_b32': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 3_2,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_b16': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 1_6,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_l14': {
        'embed_dim': 7_6_8,
        'image_resolution': 8_4_0,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 2_4,
        'vision_width': 1_0_2_4,
        'vision_patch_size': 1_4,
        'transformer_width': 7_6_8,
        'transformer_heads': 1_2,
        'transformer_layers': 1_2,
    },
}
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    List[str]=""						,   __snake_case     :    List[str]="/"       ):





  '''simple docstring'''





  lowercase				       =			[]

  for k, v in params.items():
    lowercase				       =			parent_key + sep + k if parent_key else k

    if isinstance(__snake_case						,   collections.MutableMapping       ):
      items.extend(flatten_nested_dict(__snake_case						,   __snake_case						,   sep=__snake_case       ).items()       )
    else:
      items.append((new_key, v)       )
  return dict(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  return jax.tree_util.tree_map(lambda __snake_case       : x.astype(jnp.floataa       ) if x.dtype == jnp.bfloataa else x						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_weight.chunk(3						,   dim=0       )
  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_bias.chunk(3						,   dim=0       )

  lowercase				       =			pt_attn_layer.out_proj.weight
  lowercase				       =			pt_attn_layer.out_proj.bias

  lowercase				       =			q_proj
  lowercase				       =			q_proj_bias

  lowercase				       =			k_proj
  lowercase				       =			k_proj_bias

  lowercase				       =			v_proj
  lowercase				       =			v_proj_bias

  lowercase				       =			out_proj_weights
  lowercase				       =			out_proj_bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  copy_linear(hf_mlp.fca						,   pt_mlp.c_fc       )
  copy_linear(hf_mlp.fca						,   pt_mlp.c_proj       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			pt_linear.weight
  lowercase				       =			pt_linear.bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    str       ):





  '''simple docstring'''





  copy_linear(hf_layer.layer_norma						,   pt_layer.ln_a       )
  copy_linear(hf_layer.layer_norma						,   pt_layer.ln_a       )

  # copy MLP
  copy_mlp(hf_layer.mlp						,   pt_layer.mlp       )

  # copy attn
  copy_attn_layer(hf_layer.self_attn						,   pt_layer.attn       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  for hf_layer, pt_layer in zip(__snake_case						,   __snake_case       ):
    copy_layer(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.token_embedding.weight
  lowercase				       =			pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm						,   pt_model.ln_final       )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers						,   pt_model.transformer.resblocks       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layernorm						,   pt_model.visual.ln_pre       )
  copy_linear(hf_model.vision_model.post_layernorm						,   pt_model.visual.ln_post       )

  # copy embeds
  lowercase				       =			pt_model.visual.conva.weight.data
  lowercase				       =			pt_model.visual.class_embedding
  lowercase				       =			pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers						,   pt_model.visual.transformer.resblocks       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			flatten_nested_dict(flax_params['backbone']['merged_class_token']       )

  lowercase				       =			torch.from_numpy(flax_class_token_params['scale']       )
  lowercase				       =			torch.from_numpy(flax_class_token_params['bias']       )
  lowercase				       =			nn.Parameter(__snake_case       )
  lowercase				       =			nn.Parameter(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			hf_model.state_dict()
  lowercase				       =			{}

  # Rename class prediction head flax params to pytorch HF
  lowercase				       =			flatten_nested_dict(flax_params['class_head']       )

  for flax_key, v in flax_class_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('Dense_0'						,   'dense0'       )
    lowercase				       =			'class_head.' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      lowercase				       =			v.T

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Rename box prediction box flax params to pytorch HF
  lowercase				       =			flatten_nested_dict(flax_params['obj_box_head']       )

  for flax_key, v in flax_box_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('_'						,   ''       ).lower()
    lowercase				       =			'box_head.' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      lowercase				       =			v.T

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Copy flax params to PyTorch params
  for name, param in new_params.items():
    if name in pt_params.keys():
      pt_params[name].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  for k, v in flax_attn_params.items():
    if k.startswith('transformer'       ):
      lowercase				       =			k.replace('transformer.resblocks'						,   'text_model.encoder.layers'       )
    else:
      lowercase				       =			k.replace('visual.transformer.resblocks'						,   'vision_model.encoder.layers'       )

    lowercase				       =			torch_key.replace('attn'						,   'self_attn'       )
    lowercase				       =			torch_key.replace('key'						,   'k_proj'       )
    lowercase				       =			torch_key.replace('value'						,   'v_proj'       )
    lowercase				       =			torch_key.replace('query'						,   'q_proj'       )
    lowercase				       =			torch_key.replace('out'						,   'out_proj'       )

    if "bias" in torch_key and v.ndim == 2:
      lowercase				       =			v.shape[0] * v.shape[1]
      lowercase				       =			v.reshape(__snake_case       )

    if "weight" in torch_key and "out" in torch_key:
      lowercase				       =			(v.shape[0] * v.shape[1], v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    if "weight" in torch_key and "out" not in torch_key:
      lowercase				       =			(v.shape[0], v.shape[1] * v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    # Copy flax CLIP attn params to HF PyTorch params
    lowercase				       =			torch.from_numpy(__snake_case       )
    hf_backbone.state_dict()[torch_key].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			{}
  lowercase				       =			[]

  for k, v in params.items():
    if "attn." in k:
      lowercase				       =			k[: k.rindex('attn.'       ) + 5]
      if base in processed_attn_layers:
        continue

      processed_attn_layers.append(__snake_case       )
      lowercase				       =			params[base + 'out.weight'].shape[-1]
      lowercase				       =			params[base + 'out.weight'].reshape(__snake_case						,   __snake_case       ).T
      lowercase				       =			params[base + 'out.bias']
    else:
      lowercase				       =			v
  return new_params
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			CLIP(**__snake_case       )
  torch_model.eval()
  lowercase				       =			torch_model.state_dict()

  lowercase				       =			flatten_nested_dict(flax_params['backbone']['clip']       )
  lowercase				       =			{}

  for flax_key, v in flax_clip_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('text.token_embedding.embedding'						,   'token_embedding.kernel'       )

    if (
        torch_key.startswith('text.transformer'       )
        or torch_key.startswith('text.text_projection'       )
        or torch_key.startswith('text.ln_final'       )
        or torch_key.startswith('text.positional_embedding'       )
    ):
      lowercase				       =			torch_key[5:]

    lowercase				       =			torch_key.replace('text_projection.kernel'						,   'text_projection'       )
    lowercase				       =			torch_key.replace('visual.proj.kernel'						,   'visual.proj'       )
    lowercase				       =			torch_key.replace('.scale'						,   '.weight'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      lowercase				       =			v.transpose(3						,   2						,   0						,   1       )

    elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
      # Fully connected layers are transposed, embeddings are not
      lowercase				       =			v.T

    lowercase				       =			v

  lowercase				       =			_convert_attn_layers(__snake_case       )
  new_torch_params.update(__snake_case       )
  lowercase				       =			{}

  # Copy flax CLIP backbone params to PyTorch params
  for name, param in new_torch_params.items():
    if name in torch_clip_params.keys():
      lowercase				       =			torch.from_numpy(new_torch_params[name]       )
      torch_clip_params[name].copy_(__snake_case       )
    else:
      lowercase				       =			param

  return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Tuple						,   __snake_case     :    Optional[int]						,   __snake_case     :    Tuple						,   __snake_case     :    Tuple=None       ):





  '''simple docstring'''





  lowercase				       =			Repository(__snake_case						,   clone_from=f'google/{pytorch_dump_folder_path}'       )
  repo.git_pull()

  if config_path is not None:
    lowercase				       =			OwlViTConfig.from_pretrained(__snake_case       )
  else:
    lowercase				       =			OwlViTConfig()

  lowercase				       =			OwlViTModel(__snake_case       ).eval()
  lowercase				       =			OwlViTForObjectDetection(__snake_case       ).eval()

  copy_text_model_and_projection(__snake_case						,   __snake_case       )
  copy_vision_model_and_projection(__snake_case						,   __snake_case       )
  lowercase				       =			pt_backbone.logit_scale
  copy_flax_attn_params(__snake_case						,   __snake_case       )

  lowercase				       =			hf_backbone
  copy_class_merge_token(__snake_case						,   __snake_case       )
  copy_class_box_heads(__snake_case						,   __snake_case       )

  # Save HF model
  hf_model.save_pretrained(repo.local_dir       )

  # Initialize image processor
  lowercase				       =			OwlViTImageProcessor(
      size=config.vision_config.image_size						,   crop_size=config.vision_config.image_size       )
  # Initialize tokenizer
  lowercase				       =			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						,   pad_token='!'						,   model_max_length=16       )

  # Initialize processor
  lowercase				       =			OwlViTProcessor(image_processor=__snake_case						,   tokenizer=__snake_case       )
  image_processor.save_pretrained(repo.local_dir       )
  processor.save_pretrained(repo.local_dir       )

  repo.git_add()
  repo.git_commit('Upload model and processor'       )
  repo.git_push()


if __name__ == "__main__":
    _UpperCamelCase						:    Any		=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--owlvit_version',
        default=None,
        type=str,
        required=True,
        help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
    )
    parser.add_argument(
        '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
    )
    parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
    parser.add_argument(
        '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
    )
    _UpperCamelCase						:    Tuple		=						parser.parse_args()

    # Initialize PyToch clip model
    _UpperCamelCase						:    Union[str, Any]		=						args.owlvit_version
    if model_name == "clip_b16":
        _UpperCamelCase						:    List[Any]		=						CONFIGS['vit_b16']
    elif model_name == "clip_b32":
        _UpperCamelCase						:    Optional[Any]		=						CONFIGS['vit_b32']
    elif model_name == "clip_l14":
        _UpperCamelCase						:    Any		=						CONFIGS['vit_l14']

    # Load from checkpoint and convert params to float-32
    _UpperCamelCase						:    Optional[int]		=						checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
    _UpperCamelCase						:    Optional[int]		=						jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
    del variables

    # Convert CLIP backbone
    _UpperCamelCase,						_UpperCamelCase,						_UpperCamelCase						:    Optional[Any]		=						convert_clip_backbone(flax_params, torch_config)

    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)










"""simple docstring"""



import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
    from transformers.models.flava.image_processing_flava import (
        FLAVA_CODEBOOK_MEAN,
        FLAVA_CODEBOOK_STD,
        FLAVA_IMAGE_MEAN,
        FLAVA_IMAGE_STD,
    )

@require_vision
class 			a	(  unittest.TestCase      ):







    def      UpperCamelCase_ (     self			):
      lowercase				       =			tempfile.mkdtemp()

      # fmt: off
      lowercase				       =			['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowercase				       =			os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']			)

      with open(self.vocab_file				,						'w'				,						encoding='utf-8'			) as fp:
        fp.write(''.join([x + '\n' for x in vocab_tokens]			)			)

      lowercase				       =			{
          'image_mean': FLAVA_IMAGE_MEAN,
          'image_std': FLAVA_IMAGE_STD,
          'do_normalize': True,
          'do_resize': True,
          'size': 2_2_4,
          'do_center_crop': True,
          'crop_size': 2_2_4,
          'input_size_patches': 1_4,
          'total_mask_patches': 7_5,
          'mask_group_max_patches': None,
          'mask_group_min_patches': 1_6,
          'mask_group_min_aspect_ratio': 0.3,
          'mask_group_max_aspect_ratio': None,
          'codebook_do_resize': True,
          'codebook_size': 1_1_2,
          'codebook_do_center_crop': True,
          'codebook_crop_size': 1_1_2,
          'codebook_do_map_pixels': True,
          'codebook_do_normalize': True,
          'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
          'codebook_image_std': FLAVA_CODEBOOK_STD,
      }

      lowercase				       =			os.path.join(self.tmpdirname				,						_lowerCamelCase			)
      with open(self.image_processor_file				,						'w'				,						encoding='utf-8'			) as fp:
        json.dump(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizer.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return BertTokenizerFast.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						**_lowerCamelCase			):
      return FlavaImageProcessor.from_pretrained(self.tmpdirname				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      shutil.rmtree(self.tmpdirname			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[np.random.randint(2_5_5				,						size=(3, 3_0, 4_0_0)				,						dtype=np.uinta			)]

      lowercase				       =			[Image.fromarray(np.moveaxis(_lowerCamelCase				,						0				,						-1			)			) for x in image_inputs]

      return image_inputs







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_tokenizer()
      lowercase				       =			self.get_rust_tokenizer()
      lowercase				       =			self.get_image_processor()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_slow.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.from_pretrained(self.tmpdirname				,						use_fast=_lowerCamelCase			)

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)
      processor_fast.save_pretrained(self.tmpdirname			)
      lowercase				       =			FlavaProcessor.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				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.tokenizer				,						_lowerCamelCase			)

      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				,						_lowerCamelCase			)
      self.assertIsInstance(processor_fast.image_processor				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			FlavaProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

      lowercase				       =			self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'			)
      lowercase				       =			self.get_image_processor(do_normalize=_lowerCamelCase				,						padding_value=1.0			)

      lowercase				       =			FlavaProcessor.from_pretrained(
          self.tmpdirname				,						bos_token='(BOS)'				,						eos_token='(EOS)'				,						do_normalize=_lowerCamelCase				,						padding_value=1.0			)

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

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			image_processor(_lowerCamelCase				,						return_tensors='np'			)
      lowercase				       =			processor(images=_lowerCamelCase				,						return_tensors='np'			)

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

      # With rest of the args
      random.seed(1_2_3_4			)
      lowercase				       =			image_processor(
          _lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_tensors='np'			)
      random.seed(1_2_3_4			)
      lowercase				       =			processor(
          images=_lowerCamelCase				,						return_image_mask=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						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      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'

      lowercase				       =			processor(text=_lowerCamelCase			)

      lowercase				       =			tokenizer(_lowerCamelCase			)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

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

      # add extra args
      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase				,						return_codebook_pixels=_lowerCamelCase				,						return_image_mask=_lowerCamelCase			)

      self.assertListEqual(
          list(inputs.keys()			)				,						[
              'input_ids',
              'token_type_ids',
              'attention_mask',
              'pixel_values',
              'codebook_pixel_values',
              'bool_masked_pos',
          ]				,						)

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







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowercase				       =			processor.batch_decode(_lowerCamelCase			)
      lowercase				       =			tokenizer.batch_decode(_lowerCamelCase			)

      self.assertListEqual(_lowerCamelCase				,						_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.get_image_processor()
      lowercase				       =			self.get_tokenizer()

      lowercase				       =			FlavaProcessor(tokenizer=_lowerCamelCase				,						image_processor=_lowerCamelCase			)

      lowercase				       =			'lower newer'
      lowercase				       =			self.prepare_image_inputs()

      lowercase				       =			processor(text=_lowerCamelCase				,						images=_lowerCamelCase			)

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


