
import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_UpperCAmelCase :			Tuple              =	{
    "sample_size": 32,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": 1_000,
    "block_out_channels": [32, 64],
    "attention_head_dim": 8,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_UpperCAmelCase :			Any              =	{
    "sample_size": 64,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 3,
    "num_class_embeds": 1_000,
    "block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "scale_shift",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_UpperCAmelCase :			str              =	{
    "sample_size": 256,
    "in_channels": 3,
    "out_channels": 3,
    "layers_per_block": 2,
    "num_class_embeds": None,
    "block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    "attention_head_dim": 64,
    "down_block_types": [
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "ResnetDownsampleBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
        "AttnDownBlock2D",
    ],
    "up_block_types": [
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "AttnUpBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
        "ResnetUpsampleBlock2D",
    ],
    "resnet_time_scale_shift": "default",
    "upsample_type": "resnet",
    "downsample_type": "resnet",
}

_UpperCAmelCase :			Optional[int]              =	{
    "num_train_timesteps": 40,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_UpperCAmelCase :			Union[str, Any]              =	{
    "num_train_timesteps": 201,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}

_UpperCAmelCase :			Optional[Any]              =	{
    "num_train_timesteps": 151,
    "sigma_min": 0.002,
    "sigma_max": 80.0,
}


def 				A	( lowercase			)       ->  Optional[int]:
		'''simple docstring'''

		if isinstance(A__   ,	A__			):
				return v
		if v.lower() in ("yes", "true", "t", "y", "1"):
				return True
		elif v.lower() in ("no", "false", "f", "n", "0"):
				return False
		else:
				raise argparse.ArgumentTypeError('boolean value expected'			)


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase=False			)       ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase          =					checkpoint[f'''{old_prefix}.in_layers.0.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.in_layers.0.bias''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.in_layers.2.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.in_layers.2.bias''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.out_layers.0.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.out_layers.0.bias''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.out_layers.3.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.out_layers.3.bias''']

		if has_skip:
				UpperCamelCase          =					checkpoint[f'''{old_prefix}.skip_connection.weight''']
				UpperCamelCase          =					checkpoint[f'''{old_prefix}.skip_connection.bias''']

		return new_checkpoint


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase=None			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3   ,	dim=0			)
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3   ,	dim=0			)

		UpperCamelCase          =					checkpoint[f'''{old_prefix}.norm.weight''']
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.norm.bias''']

		UpperCamelCase          =					weight_q.squeeze(-1			).squeeze(-1			)
		UpperCamelCase          =					bias_q.squeeze(-1			).squeeze(-1			)
		UpperCamelCase          =					weight_k.squeeze(-1			).squeeze(-1			)
		UpperCamelCase          =					bias_k.squeeze(-1			).squeeze(-1			)
		UpperCamelCase          =					weight_v.squeeze(-1			).squeeze(-1			)
		UpperCamelCase          =					bias_v.squeeze(-1			).squeeze(-1			)

		UpperCamelCase          =					(
		    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1			).squeeze(-1			)
		)
		UpperCamelCase          =					checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1			).squeeze(-1			)

		return new_checkpoint


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					torch.load(A__   ,	map_location='cpu'			)
		UpperCamelCase          =					{}

		UpperCamelCase          =					checkpoint["""time_embed.0.weight"""]
		UpperCamelCase          =					checkpoint["""time_embed.0.bias"""]
		UpperCamelCase          =					checkpoint["""time_embed.2.weight"""]
		UpperCamelCase          =					checkpoint["""time_embed.2.bias"""]

		if unet_config["num_class_embeds"] is not None:
				UpperCamelCase          =					checkpoint["""label_emb.weight"""]

		UpperCamelCase          =					checkpoint["""input_blocks.0.0.weight"""]
		UpperCamelCase          =					checkpoint["""input_blocks.0.0.bias"""]

		UpperCamelCase          =					unet_config["""down_block_types"""]
		UpperCamelCase          =					unet_config["""layers_per_block"""]
		UpperCamelCase          =					unet_config["""attention_head_dim"""]
		UpperCamelCase          =					unet_config["""block_out_channels"""]
		UpperCamelCase          =					1
		UpperCamelCase          =					channels_list[0]

		for i, layer_type in enumerate(A__			):
				UpperCamelCase          =					channels_list[i]
				UpperCamelCase          =					current_channels != prev_channels
				if layer_type == "ResnetDownsampleBlock2D":
						for j in range(A__			):
								UpperCamelCase          =					f'''down_blocks.{i}.resnets.{j}'''
								UpperCamelCase          =					f'''input_blocks.{current_layer}.0'''
								UpperCamelCase          =					True if j == 0 and downsample_block_has_skip else False
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__   ,	has_skip=A__			)
								current_layer += 1

				elif layer_type == "AttnDownBlock2D":
						for j in range(A__			):
								UpperCamelCase          =					f'''down_blocks.{i}.resnets.{j}'''
								UpperCamelCase          =					f'''input_blocks.{current_layer}.0'''
								UpperCamelCase          =					True if j == 0 and downsample_block_has_skip else False
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__   ,	has_skip=A__			)
								UpperCamelCase          =					f'''down_blocks.{i}.attentions.{j}'''
								UpperCamelCase          =					f'''input_blocks.{current_layer}.1'''
								UpperCamelCase          =					convert_attention(
								    A__   ,	A__   ,	A__   ,	A__   ,	A__			)
								current_layer += 1

				if i != len(A__			) - 1:
						UpperCamelCase          =					f'''down_blocks.{i}.downsamplers.0'''
						UpperCamelCase          =					f'''input_blocks.{current_layer}.0'''
						UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__			)
						current_layer += 1

				UpperCamelCase          =					current_channels

		# hardcoded the mid-block for now
		UpperCamelCase          =					"""mid_block.resnets.0"""
		UpperCamelCase          =					"""middle_block.0"""
		UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__			)
		UpperCamelCase          =					"""mid_block.attentions.0"""
		UpperCamelCase          =					"""middle_block.1"""
		UpperCamelCase          =					convert_attention(A__   ,	A__   ,	A__   ,	A__   ,	A__			)
		UpperCamelCase          =					"""mid_block.resnets.1"""
		UpperCamelCase          =					"""middle_block.2"""
		UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__			)

		UpperCamelCase          =					0
		UpperCamelCase          =					unet_config["""up_block_types"""]

		for i, layer_type in enumerate(A__			):
				if layer_type == "ResnetUpsampleBlock2D":
						for j in range(layers_per_block + 1			):
								UpperCamelCase          =					f'''up_blocks.{i}.resnets.{j}'''
								UpperCamelCase          =					f'''output_blocks.{current_layer}.0'''
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__   ,	has_skip=A__			)
								current_layer += 1

						if i != len(A__			) - 1:
								UpperCamelCase          =					f'''up_blocks.{i}.upsamplers.0'''
								UpperCamelCase          =					f'''output_blocks.{current_layer-1}.1'''
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__			)
				elif layer_type == "AttnUpBlock2D":
						for j in range(layers_per_block + 1			):
								UpperCamelCase          =					f'''up_blocks.{i}.resnets.{j}'''
								UpperCamelCase          =					f'''output_blocks.{current_layer}.0'''
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__   ,	has_skip=A__			)
								UpperCamelCase          =					f'''up_blocks.{i}.attentions.{j}'''
								UpperCamelCase          =					f'''output_blocks.{current_layer}.1'''
								UpperCamelCase          =					convert_attention(
								    A__   ,	A__   ,	A__   ,	A__   ,	A__			)
								current_layer += 1

						if i != len(A__			) - 1:
								UpperCamelCase          =					f'''up_blocks.{i}.upsamplers.0'''
								UpperCamelCase          =					f'''output_blocks.{current_layer-1}.2'''
								UpperCamelCase          =					convert_resnet(A__   ,	A__   ,	A__   ,	A__			)

		UpperCamelCase          =					checkpoint["""out.0.weight"""]
		UpperCamelCase          =					checkpoint["""out.0.bias"""]
		UpperCamelCase          =					checkpoint["""out.2.weight"""]
		UpperCamelCase          =					checkpoint["""out.2.bias"""]

		return new_checkpoint


if __name__ == "__main__":
					_UpperCAmelCase :			Optional[Any]              =	argparse.ArgumentParser()

					parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
					parser.add_argument(
					    "--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
					)
					parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")

					_UpperCAmelCase :			Optional[int]              =	parser.parse_args()
					_UpperCAmelCase :			List[Any]              =	strabool(args.class_cond)

					_UpperCAmelCase :			str              =	os.path.basename(args.unet_path)
					print(F'''Checkpoint: {ckpt_name}''')

					# Get U-Net config
					if "imagenet64" in ckpt_name:
										_UpperCAmelCase :			List[str]              =	IMAGENET_64_UNET_CONFIG
					elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										_UpperCAmelCase :			int              =	LSUN_256_UNET_CONFIG
					elif "test" in ckpt_name:
										_UpperCAmelCase :			Union[str, Any]              =	TEST_UNET_CONFIG
					else:
										raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

					if not args.class_cond:
										_UpperCAmelCase :			List[str]              =	None

					_UpperCAmelCase :			Dict              =	con_pt_to_diffuser(args.unet_path, unet_config)

					_UpperCAmelCase :			Tuple              =	UNetaDModel(**unet_config)
					image_unet.load_state_dict(converted_unet_ckpt)

					# Get scheduler config
					if "cd" in ckpt_name or "test" in ckpt_name:
										_UpperCAmelCase :			int              =	CD_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
										_UpperCAmelCase :			Union[str, Any]              =	CT_IMAGENET_64_SCHEDULER_CONFIG
					elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
										_UpperCAmelCase :			Optional[int]              =	CT_LSUN_256_SCHEDULER_CONFIG
					else:
										raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

					_UpperCAmelCase :			Optional[Any]              =	CMStochasticIterativeScheduler(**scheduler_config)

					_UpperCAmelCase :			Tuple              =	ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
					consistency_model.save_pretrained(args.dump_path)


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     lowercase (		unittest.TestCase       ):



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					tempfile.mkdtemp()

				# fmt: off
				UpperCamelCase          =					['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
				# fmt: on
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)

				UpperCamelCase          =					{
				    'do_resize': True,
				    'size': 20,
				    'do_center_crop': True,
				    'crop_size': 18,
				    'do_normalize': True,
				    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
				    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
				}
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				A_	)
				with open(self.image_processor_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						json.dump(A_    ,				A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return CLIPTokenizer.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Tuple:
				"""simple docstring"""
				return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,				pad_token='!'    ,				**A_	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> Union[str, Any]:
				"""simple docstring"""
				return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				shutil.rmtree(self.tmpdirname	)



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[np.random.randint(255    ,				size=(3, 30, 400)    ,				dtype=np.uinta	)]

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

				return image_inputs



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_tokenizer()
				UpperCamelCase          =					self.get_rust_tokenizer()
				UpperCamelCase          =					self.get_image_processor()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_slow.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname    ,				use_fast=A_	)

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)
				processor_fast.save_pretrained(self.tmpdirname	)
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(self.tmpdirname	)

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

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



		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,				image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				UpperCamelCase          =					self.get_tokenizer(bos_token='(BOS)'    ,				eos_token='(EOS)'	)
				UpperCamelCase          =					self.get_image_processor(do_normalize=A_	)

				UpperCamelCase          =					OwlViTProcessor.from_pretrained(
				    self.tmpdirname    ,				bos_token='(BOS)'    ,				eos_token='(EOS)'    ,				do_normalize=A_	)

				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 __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					image_processor(A_    ,				return_tensors='np'	)
				UpperCamelCase          =					processor(images=A_    ,				return_tensors='np'	)

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



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					'lower newer'

				UpperCamelCase          =					processor(text=A_    ,				return_tensors='np'	)

				UpperCamelCase          =					tokenizer(A_    ,				return_tensors='np'	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key][0].tolist()    ,				encoded_processor[key][0].tolist()	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

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

				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)

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



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					[['cat', 'nasa badge'], ['person']]
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					len(A_	)
				UpperCamelCase          =					max([len(A_	) for texts in input_texts]	)

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(batch_size * num_max_text_queries, seq_length)	)

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					'google/owlvit-base-patch32'
				UpperCamelCase          =					OwlViTProcessor.from_pretrained(A_	)

				UpperCamelCase          =					['cat', 'nasa badge']
				UpperCamelCase          =					processor(text=A_	)

				UpperCamelCase          =					16
				UpperCamelCase          =					inputs['input_ids']
				UpperCamelCase          =					[
				    [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				    [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
				]

				self.assertListEqual(list(inputs.keys()	)    ,				['input_ids', 'attention_mask']	)
				self.assertEqual(inputs['input_ids'].shape    ,				(2, seq_length)	)
				self.assertListEqual(list(input_ids[0]	)    ,				predicted_ids[0]	)
				self.assertListEqual(list(input_ids[1]	)    ,				predicted_ids[1]	)



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

				UpperCamelCase          =					self.prepare_image_inputs()
				UpperCamelCase          =					self.prepare_image_inputs()

				UpperCamelCase          =					processor(images=A_    ,				query_images=A_	)

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

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



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					self.get_image_processor()
				UpperCamelCase          =					self.get_tokenizer()

				UpperCamelCase          =					OwlViTProcessor(tokenizer=A_    ,				image_processor=A_	)

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

				UpperCamelCase          =					processor.batch_decode(A_	)
				UpperCamelCase          =					tokenizer.batch_decode(A_	)

				self.assertListEqual(A_    ,				A_	)
