'''simple docstring'''




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


__snake_case   :      Optional[Any]						    =  "pcuenq/k-upscaler"





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Tuple,   *, _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Dict   )	->      Optional[int]:
    A_     =   {
        # norm1
        F'''{diffusers_resnet_prefix}.norm1.linear.weight''': checkpoint[F'''{resnet_prefix}.main.0.mapper.weight'''],
        F'''{diffusers_resnet_prefix}.norm1.linear.bias''': checkpoint[F'''{resnet_prefix}.main.0.mapper.bias'''],
        # conv1
        F'''{diffusers_resnet_prefix}.conv1.weight''': checkpoint[F'''{resnet_prefix}.main.2.weight'''],
        F'''{diffusers_resnet_prefix}.conv1.bias''': checkpoint[F'''{resnet_prefix}.main.2.bias'''],
        # norm2
        F'''{diffusers_resnet_prefix}.norm2.linear.weight''': checkpoint[F'''{resnet_prefix}.main.4.mapper.weight'''],
        F'''{diffusers_resnet_prefix}.norm2.linear.bias''': checkpoint[F'''{resnet_prefix}.main.4.mapper.bias'''],
        # conv2
        F'''{diffusers_resnet_prefix}.conv2.weight''': checkpoint[F'''{resnet_prefix}.main.6.weight'''],
        F'''{diffusers_resnet_prefix}.conv2.bias''': checkpoint[F'''{resnet_prefix}.main.6.bias'''],
    }

    if resnet.conv_shortcut is not None:
        rv.update(
            {
                F'''{diffusers_resnet_prefix}.conv_shortcut.weight''': checkpoint[F'''{resnet_prefix}.skip.weight'''],
            }   )

    return rv





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[Any],   *, _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	int   )	->      List[Any]:
    A_     =   checkpoint[F'''{attention_prefix}.qkv_proj.weight'''].chunk(3,   dim=0   )
    A_     =   checkpoint[F'''{attention_prefix}.qkv_proj.bias'''].chunk(3,   dim=0   )
    A_     =   {
        # norm
        F'''{diffusers_attention_prefix}.norm1.linear.weight''': checkpoint[F'''{attention_prefix}.norm_in.mapper.weight'''],
        F'''{diffusers_attention_prefix}.norm1.linear.bias''': checkpoint[F'''{attention_prefix}.norm_in.mapper.bias'''],
        # to_q
        F'''{diffusers_attention_prefix}.attn1.to_q.weight''': weight_q.squeeze(-1   ).squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn1.to_q.bias''': bias_q,
        # to_k
        F'''{diffusers_attention_prefix}.attn1.to_k.weight''': weight_k.squeeze(-1   ).squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn1.to_k.bias''': bias_k,
        # to_v
        F'''{diffusers_attention_prefix}.attn1.to_v.weight''': weight_v.squeeze(-1   ).squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn1.to_v.bias''': bias_v,
        # to_out
        F'''{diffusers_attention_prefix}.attn1.to_out.0.weight''': checkpoint[F'''{attention_prefix}.out_proj.weight''']
        .squeeze(-1   )
        .squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn1.to_out.0.bias''': checkpoint[F'''{attention_prefix}.out_proj.bias'''],
    }

    return rv





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str],   *, _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Dict   )	->      List[str]:
    A_     =   checkpoint[F'''{attention_prefix}.kv_proj.weight'''].chunk(2,   dim=0   )
    A_     =   checkpoint[F'''{attention_prefix}.kv_proj.bias'''].chunk(2,   dim=0   )

    A_     =   {
        # norm2 (ada groupnorm)
        F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight''': checkpoint[
            F'''{attention_prefix}.norm_dec.mapper.weight'''
        ],
        F'''{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias''': checkpoint[
            F'''{attention_prefix}.norm_dec.mapper.bias'''
        ],
        # layernorm on encoder_hidden_state
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight''': checkpoint[
            F'''{attention_prefix}.norm_enc.weight'''
        ],
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias''': checkpoint[
            F'''{attention_prefix}.norm_enc.bias'''
        ],
        # to_q
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight''': checkpoint[
            F'''{attention_prefix}.q_proj.weight'''
        ]
        .squeeze(-1   )
        .squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias''': checkpoint[
            F'''{attention_prefix}.q_proj.bias'''
        ],
        # to_k
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight''': weight_k.squeeze(-1   ).squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias''': bias_k,
        # to_v
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight''': weight_v.squeeze(-1   ).squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias''': bias_v,
        # to_out
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight''': checkpoint[
            F'''{attention_prefix}.out_proj.weight'''
        ]
        .squeeze(-1   )
        .squeeze(-1   ),
        F'''{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias''': checkpoint[
            F'''{attention_prefix}.out_proj.bias'''
        ],
    }

    return rv





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Optional[int]   )	->      Optional[Any]:
    A_     =   'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
    A_     =   F'''{block_prefix}.{block_idx}'''

    A_     =   {}

    if not hasattr(_lowerCamelCase,   '''attentions'''   ):
        A_     =   1  # resnet only
    elif not block.attentions[0].add_self_attention:
        A_     =   2  # resnet -> cross-attention
    else:
        A_     =   3  # resnet -> self-attention -> cross-attention)

    for resnet_idx, resnet in enumerate(block.resnets   ):
        # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
        A_     =   F'''{block_type}_blocks.{block_idx}.resnets.{resnet_idx}'''
        A_     =   n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
        A_     =   F'''{block_prefix}.{idx}''' if block_type == 'up' else F'''{block_prefix}.{idx}'''

        diffusers_checkpoint.update(
            resnet_to_diffusers_checkpoint(
                _lowerCamelCase,   _lowerCamelCase,   diffusers_resnet_prefix=_lowerCamelCase,   resnet_prefix=_lowerCamelCase   )   )

    if hasattr(_lowerCamelCase,   '''attentions'''   ):
        for attention_idx, attention in enumerate(block.attentions   ):
            A_     =   F'''{block_type}_blocks.{block_idx}.attentions.{attention_idx}'''
            A_     =   n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
            A_     =   F'''{block_prefix}.{idx}'''
            A_     =   F'''{block_prefix}.{idx }'''
            A_     =   1 if not attention.add_self_attention else 2
            A_     =   (
                n * attention_idx + cross_attention_index
                if block_type == 'up'
                else n * attention_idx + cross_attention_index + 1
            )
            A_     =   F'''{block_prefix}.{idx }'''

            diffusers_checkpoint.update(
                cross_attn_to_diffusers_checkpoint(
                    _lowerCamelCase,   diffusers_attention_prefix=_lowerCamelCase,   diffusers_attention_index=2,   attention_prefix=_lowerCamelCase,   )   )

            if attention.add_self_attention is True:
                diffusers_checkpoint.update(
                    self_attn_to_diffusers_checkpoint(
                        _lowerCamelCase,   diffusers_attention_prefix=_lowerCamelCase,   attention_prefix=_lowerCamelCase,   )   )

    return diffusers_checkpoint





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	int   )	->      Any:
    A_     =   {}

    # pre-processing
    diffusers_checkpoint.update(
        {
            '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
            '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
        }   )

    # timestep and class embedding
    diffusers_checkpoint.update(
        {
            '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1   ),
            '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
            '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
            '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
            '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
            '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
        }   )

    # down_blocks
    for down_block_idx, down_block in enumerate(model.down_blocks   ):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowerCamelCase,   _lowerCamelCase,   _lowerCamelCase,   '''down'''   )   )

    # up_blocks
    for up_block_idx, up_block in enumerate(model.up_blocks   ):
        diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowerCamelCase,   _lowerCamelCase,   _lowerCamelCase,   '''up'''   )   )

    # post-processing
    diffusers_checkpoint.update(
        {
            '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
            '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
        }   )

    return diffusers_checkpoint





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any]   )	->      Optional[Any]:
    A_     =   original_config['input_channels'] + original_config['unet_cond_dim']
    A_     =   original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

    A_     =   original_config['channels']

    assert (
        len(set(original_config['''depths''']   )   ) == 1
    ), "UNet2DConditionModel currently do not support blocks with different number of layers"
    A_     =   original_config['depths'][0]

    A_     =   original_config['mapping_cond_dim']
    A_     =   original_config['cross_cond_dim']

    A_     =   []
    A_     =   []
    for s, c in zip(original_config['''self_attn_depths'''],   original_config['''cross_attn_depths''']   ):
        if s:
            A_     =   'self'
            A_     =   'cross' if c else None
        elif c:
            A_     =   'cross'
            A_     =   None
        else:
            A_     =   None
            A_     =   None
        attna_types.append(_lowerCamelCase   )
        attna_types.append(_lowerCamelCase   )

    A_     =   UNetaDConditionModel(
        in_channels=_lowerCamelCase,   out_channels=_lowerCamelCase,   down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D'''),   mid_block_type=_lowerCamelCase,   up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D'''),   block_out_channels=_lowerCamelCase,   layers_per_block=_lowerCamelCase,   act_fn='''gelu''',   norm_num_groups=_lowerCamelCase,   cross_attention_dim=_lowerCamelCase,   attention_head_dim=64,   time_cond_proj_dim=_lowerCamelCase,   resnet_time_scale_shift='''scale_shift''',   time_embedding_type='''fourier''',   timestep_post_act='''gelu''',   conv_in_kernel=1,   conv_out_kernel=1,   )

    return unet





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any]   )	->      Dict:
    A_     =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''   )

    A_     =   huggingface_hub.hf_hub_download(_lowerCamelCase,   '''config_laion_text_cond_latent_upscaler_2.json'''   )
    A_     =   huggingface_hub.hf_hub_download(
        _lowerCamelCase,   '''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''   )
    print(F'''loading original model configuration from {orig_config_path}'''   )
    print(F'''loading original model checkpoint from {orig_weights_path}'''   )

    print('''converting to diffusers unet'''   )
    A_     =   K.config.load_config(open(_lowerCamelCase   )   )['model']
    A_     =   unet_model_from_original_config(_lowerCamelCase   )

    A_     =   torch.load(_lowerCamelCase,   map_location=_lowerCamelCase   )['model_ema']
    A_     =   unet_to_diffusers_checkpoint(_lowerCamelCase,   _lowerCamelCase   )

    model.load_state_dict(_lowerCamelCase,   strict=_lowerCamelCase   )
    model.save_pretrained(args.dump_path   )
    print(F'''saving converted unet model in {args.dump_path}'''   )


if __name__ == "__main__":
  __snake_case   :      Optional[int]						    =  argparse.ArgumentParser()

  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  __snake_case   :      Any						    =  parser.parse_args()

  main(args)














"""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 ):







      '''simple docstring'''

      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						tempfile.mkdtemp()

       # fmt: off
       lowerCamelCase__     :			List[str]     				=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
       # fmt: on
       lowerCamelCase__     :			List[Any]     				=						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]					)					)

       lowerCamelCase__     :			List[Any]     				=						{
           '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,
       }

       lowerCamelCase__     :			List[str]     				=						os.path.join(self.tmpdirname,     lowerCamelCase_					)
       with open(self.image_processor_file,     'w',     encoding='utf-8'					) as fp:
        json.dump(lowerCamelCase_,     lowerCamelCase_					)




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



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




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



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




      def 			a__							(self,     **lowerCamelCase_					):

       '''simple docstring'''



       return FlavaImageProcessor.from_pretrained(self.tmpdirname,     **lowerCamelCase_					)




      def 			a__							(self					):

       '''simple docstring'''



       shutil.rmtree(self.tmpdirname					)




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						[np.random.randint(2_5_5,     size=(3, 3_0, 4_0_0),     dtype=np.uinta					)]

       lowerCamelCase__     :			Union[str, Any]     				=						[Image.fromarray(np.moveaxis(lowerCamelCase_,     0,     -1					)					) for x in image_inputs]

       return image_inputs




      def 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Union[str, Any]     				=						self.get_tokenizer()
       lowerCamelCase__     :			Dict     				=						self.get_rust_tokenizer()
       lowerCamelCase__     :			Union[str, Any]     				=						self.get_image_processor()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_slow.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			str     				=						FlavaProcessor.from_pretrained(self.tmpdirname,     use_fast=lowerCamelCase_					)

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)
       processor_fast.save_pretrained(self.tmpdirname					)
       lowerCamelCase__     :			List[str]     				=						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 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=self.get_tokenizer(),     image_processor=self.get_image_processor()					)
       processor.save_pretrained(self.tmpdirname					)

       lowerCamelCase__     :			List[str]     				=						self.get_tokenizer(bos_token='(BOS)',     eos_token='(EOS)'					)
       lowerCamelCase__     :			List[str]     				=						self.get_image_processor(do_normalize=lowerCamelCase_,     padding_value=1.0					)

       lowerCamelCase__     :			Dict     				=						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 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Optional[Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Tuple     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			List[str]     				=						image_processor(lowerCamelCase_,     return_tensors='np'					)
       lowerCamelCase__     :			Optional[Any]     				=						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					)
       lowerCamelCase__     :			Optional[Any]     				=						image_processor(
           lowerCamelCase_,     return_image_mask=lowerCamelCase_,     return_codebook_pixels=lowerCamelCase_,     return_tensors='np'					)
       random.seed(1_2_3_4					)
       lowerCamelCase__     :			Optional[Any]     				=						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 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			Optional[int]     				=						'lower newer'

       lowerCamelCase__     :			Union[str, Any]     				=						processor(text=lowerCamelCase_					)

       lowerCamelCase__     :			Union[str, Any]     				=						tokenizer(lowerCamelCase_					)

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




      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			List[str]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			str     				=						'lower newer'
       lowerCamelCase__     :			Union[str, Any]     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			Optional[int]     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_					)

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

       # add extra args
       lowerCamelCase__     :			Tuple     				=						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 			a__							(self					):

       '''simple docstring'''



       lowerCamelCase__     :			int     				=						self.get_image_processor()
       lowerCamelCase__     :			List[str]     				=						self.get_tokenizer()

       lowerCamelCase__     :			Tuple     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

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

       lowerCamelCase__     :			str     				=						processor.batch_decode(lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						tokenizer.batch_decode(lowerCamelCase_					)

       self.assertListEqual(lowerCamelCase_,     lowerCamelCase_					)





      def 			a__							(self					):

       '''simple docstring'''



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

       lowerCamelCase__     :			Union[str, Any]     				=						FlavaProcessor(tokenizer=lowerCamelCase_,     image_processor=lowerCamelCase_					)

       lowerCamelCase__     :			List[Any]     				=						'lower newer'
       lowerCamelCase__     :			Optional[int]     				=						self.prepare_image_inputs()

       lowerCamelCase__     :			List[str]     				=						processor(text=lowerCamelCase_,     images=lowerCamelCase_					)

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

