




"""simple docstring"""

import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=False					, _UpperCAmelCase=False		):




	"""simple docstring"""






	A_					:			List[str]						=						'''backbone.''' if is_semantic else ''''''

	A_					:			List[Any]						=						[]
	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"""beit.encoder.layer.{i}.layernorm_before.weight""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""")		)
		rename_keys.append(
		    (f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""")		)
		rename_keys.append(
		    (f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""")		)
		rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""")		)

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

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ('''mask_token''', '''beit.embeddings.mask_token'''),
		        (
		            '''rel_pos_bias.relative_position_bias_table''',
		            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
		        ),
		        (
		            '''rel_pos_bias.relative_position_index''',
		            '''beit.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''', '''beit.pooler.layernorm.weight'''),
		        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
		        ('''head.weight''', '''classifier.weight'''),
		        ('''head.bias''', '''classifier.bias'''),
		    ]		)

	return rename_keys

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase=False					, _UpperCAmelCase=False		):




	"""simple docstring"""






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

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

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

		A_					:			Tuple						=						gamma_a
		A_					:			Union[str, Any]						=						gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			A_					:			Optional[int]						=						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_bias_table"""		)
			A_					:			List[str]						=						state_dict.pop(f"""{prefix}blocks.{i}.attn.relative_position_index"""		)

			A_					:			Optional[Any]						=						table
			A_					:			Any						=						index

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Any						=						dct.pop(_UpperCAmelCase		)
	A_					:			Any						=						val

def 						lowercase_ (						):




	"""simple docstring"""






	A_					:			List[str]						=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	A_					:			Tuple						=						Image.open(requests.get(_UpperCAmelCase					, stream=_UpperCAmelCase		).raw		)
	return im

@torch.no_grad()
def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						BeitConfig()
	A_					:			Tuple						=						False
	A_					:			str						=						False
	A_					:			str						=						'''huggingface/label-files'''
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		A_					:			List[Any]						=						True
		A_					:			Optional[Any]						=						True
		A_					:			Optional[int]						=						True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		A_					:			List[str]						=						True
		A_					:			int						=						2_1841
		A_					:			Optional[Any]						=						'''imagenet-22k-id2label.json'''
		A_					:			Tuple						=						json.load(open(hf_hub_download(_UpperCAmelCase					, _UpperCAmelCase					, repo_type='''dataset'''		)					, '''r'''		)		)
		A_					:			Dict						=						{int(_UpperCAmelCase		): v for k, v in idalabel.items()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9205]
		del idalabel[1_5027]
		A_					:			Any						=						idalabel
		A_					:			int						=						{v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		A_					:			Union[str, Any]						=						True
		A_					:			int						=						1000
		A_					:			List[Any]						=						'''imagenet-1k-id2label.json'''
		A_					:			List[Any]						=						json.load(open(hf_hub_download(_UpperCAmelCase					, _UpperCAmelCase					, repo_type='''dataset'''		)					, '''r'''		)		)
		A_					:			Any						=						{int(_UpperCAmelCase		): v for k, v in idalabel.items()}
		A_					:			Tuple						=						idalabel
		A_					:			Optional[Any]						=						{v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			A_					:			Tuple						=						384
		if "512" in checkpoint_url:
			A_					:			Union[str, Any]						=						512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		A_					:			Union[str, Any]						=						True
		A_					:			List[Any]						=						150
		A_					:			Union[str, Any]						=						'''ade20k-id2label.json'''
		A_					:			List[Any]						=						json.load(open(hf_hub_download(_UpperCAmelCase					, _UpperCAmelCase					, repo_type='''dataset'''		)					, '''r'''		)		)
		A_					:			Any						=						{int(_UpperCAmelCase		): v for k, v in idalabel.items()}
		A_					:			Union[str, Any]						=						idalabel
		A_					:			Any						=						{v: k for k, v in idalabel.items()}
		A_					:			Optional[int]						=						640
		A_					:			List[Any]						=						True
	else:
		raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''		)

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		A_					:			List[Any]						=						1024
		A_					:			List[Any]						=						4096
		A_					:			Any						=						24
		A_					:			Optional[Any]						=						16
		if "ade20k" in checkpoint_url:
			A_					:			Optional[Any]						=						640
			A_					:			str						=						[7, 11, 15, 23]
	else:
		raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

	# load state_dict of original model, remove and rename some keys
	A_					:			Optional[Any]						=						torch.hub.load_state_dict_from_url(_UpperCAmelCase					, map_location='''cpu'''					, check_hash=_UpperCAmelCase		)
	A_					:			Any						=						state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

	A_					:			List[Any]						=						create_rename_keys(_UpperCAmelCase					, has_lm_head=_UpperCAmelCase					, is_semantic=_UpperCAmelCase		)
	for src, dest in rename_keys:
		rename_key(_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase		)
	read_in_q_k_v(_UpperCAmelCase					, _UpperCAmelCase					, has_lm_head=_UpperCAmelCase					, is_semantic=_UpperCAmelCase		)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			A_					:			int						=						state_dict.pop(_UpperCAmelCase		)
			if key.startswith('''backbone.fpn'''		):
				A_					:			int						=						key.replace('''backbone.fpn'''					, '''fpn'''		)
			A_					:			List[Any]						=						val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		A_					:			Union[str, Any]						=						BeitForMaskedImageModeling(_UpperCAmelCase		)
	elif "ade20k" in checkpoint_url:
		A_					:			Dict						=						BeitForSemanticSegmentation(_UpperCAmelCase		)
	else:
		A_					:			Optional[int]						=						BeitForImageClassification(_UpperCAmelCase		)
	model.eval()
	model.load_state_dict(_UpperCAmelCase		)

	# Check outputs on an image
	if is_semantic:
		A_					:			Tuple						=						BeitImageProcessor(size=config.image_size					, do_center_crop=_UpperCAmelCase		)
		A_					:			Dict						=						load_dataset('''hf-internal-testing/fixtures_ade20k'''					, split='''test'''		)
		A_					:			List[Any]						=						Image.open(ds[0]['''file''']		)
	else:
		A_					:			int						=						BeitImageProcessor(
		    size=config.image_size					, resample=PILImageResampling.BILINEAR					, do_center_crop=_UpperCAmelCase		)
		A_					:			Dict						=						prepare_img()

	A_					:			int						=						image_processor(images=_UpperCAmelCase					, return_tensors='''pt'''		)
	A_					:			Union[str, Any]						=						encoding['''pixel_values''']

	A_					:			List[str]						=						model(_UpperCAmelCase		)
	A_					:			Tuple						=						outputs.logits

	# verify logits
	A_					:			Any						=						torch.Size([1, 1000]		)
	if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''		):
		A_					:			Union[str, Any]						=						torch.Size([1, 196, 8192]		)
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''		):
		A_					:			Tuple						=						torch.Size([1, 196, 8192]		)
	elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''		):
		A_					:			List[str]						=						torch.Size([1, 2_1841]		)
		A_					:			Optional[int]						=						torch.tensor([2.2288, 2.4671, 0.7395]		)
		A_					:			List[Any]						=						2397
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''		):
		A_					:			Any						=						torch.Size([1, 2_1841]		)
		A_					:			Any						=						torch.tensor([1.6881, -0.2787, 0.5901]		)
		A_					:			str						=						2396
	elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''		):
		A_					:			Any						=						torch.tensor([0.1241, 0.0798, -0.6569]		)
		A_					:			str						=						285
	elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''		):
		A_					:			Dict						=						torch.tensor([-1.2385, -1.0987, -1.0108]		)
		A_					:			List[Any]						=						281
	elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''		):
		A_					:			Optional[Any]						=						torch.tensor([-1.5303, -0.9484, -0.3147]		)
		A_					:			List[Any]						=						761
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''		):
		A_					:			List[str]						=						torch.tensor([0.4610, -0.0928, 0.2086]		)
		A_					:			Union[str, Any]						=						761
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''		):
		A_					:			Any						=						torch.tensor([-0.4804, 0.6257, -0.1837]		)
		A_					:			Any						=						761
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''		):
		A_					:			str						=						torch.tensor([[-0.5122, 0.5117, -0.2113]]		)
		A_					:			Tuple						=						761
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''		):
		A_					:			List[str]						=						torch.tensor([-0.3062, 0.7261, 0.4852]		)
		A_					:			List[Any]						=						761
	elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''		):
		A_					:			Dict						=						(1, 150, 160, 160)
		A_					:			Tuple						=						torch.tensor(
		    [
		        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
		        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
		        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
		    ]		)
	elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''		):
		A_					:			Tuple						=						(1, 150, 160, 160)
		A_					:			Dict						=						torch.tensor(
		    [
		        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
		        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
		        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
		    ]		)
	else:
		raise ValueError('''Can\'t verify logits as model is not supported'''		)

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]					, _UpperCAmelCase					, atol=1E-3		), "First elements of logits not as expected"
		else:
			print('''Predicted class idx:'''					, logits.argmax(-1		).item()		)
			assert torch.allclose(
			    logits[0, :3]					, _UpperCAmelCase					, atol=1E-3		), "First elements of logits not as expected"
			assert logits.argmax(-1		).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
		_lowerCamelCase					:     int				       =    argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
		    type=str,
		    help='URL to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		_lowerCamelCase					:     Any				       =    parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











"""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     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

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

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

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

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.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				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			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    )







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

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

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

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







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

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

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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

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







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

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

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

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

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

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





