

import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCAmelCase     :			Tuple    =logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class __UpperCamelCase    (							_a		):






					'''simple docstring'''



					__magic_name__     			=      42
					__magic_name__     			=      None



def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=0.9_99  ,SCREAMING_SNAKE_CASE="cosine"  ,):
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(SCREAMING_SNAKE_CASE			):
			return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(SCREAMING_SNAKE_CASE			):
			return math.exp(t * -12.0			)

	else:
		raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"			)

	UpperCAmelCase__:  Union[str, Any]		       =   []
	for i in range(SCREAMING_SNAKE_CASE			):
		UpperCAmelCase__:  List[str]		       =   i / num_diffusion_timesteps
		UpperCAmelCase__:  List[str]		       =   (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE			) / alpha_bar_fn(SCREAMING_SNAKE_CASE			)  ,SCREAMING_SNAKE_CASE			)			)
	return torch.tensor(SCREAMING_SNAKE_CASE  ,dtype=torch.floataa			)

class __UpperCamelCase    (							_a				,_a		):






					'''simple docstring'''



					__magic_name__     			=      [e.name for e in KarrasDiffusionSchedulers]
					__magic_name__     			=      1





					@register_to_config
					def __init__(							self							,			lowerCamelCase__ = 1_0_0_0							,			lowerCamelCase__ = 0.0_001							,			lowerCamelCase__ = 0.02							,			lowerCamelCase__ = "linear"							,			lowerCamelCase__ = None							,			lowerCamelCase__ = "epsilon"							,			lowerCamelCase__ = "linspace"							,			lowerCamelCase__ = 0							,			):
						if trained_betas is not None:
							UpperCAmelCase__:  List[Any]		       =   torch.tensor(lowerCamelCase__							,			dtype=torch.floataa	)
						elif beta_schedule == "linear":
							UpperCAmelCase__:  List[str]		       =   torch.linspace(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			dtype=torch.floataa	)
						elif beta_schedule == "scaled_linear":
							# this schedule is very specific to the latent diffusion model.
							UpperCAmelCase__:  Any		       =   (
							    torch.linspace(beta_start**0.5							,			beta_end**0.5							,			lowerCamelCase__							,			dtype=torch.floataa	) ** 2
							)
						elif beta_schedule == "squaredcos_cap_v2":
							# Glide cosine schedule
							UpperCAmelCase__:  int		       =   betas_for_alpha_bar(lowerCamelCase__	)
						else:
							raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"	)

						UpperCAmelCase__:  Union[str, Any]		       =   1.0 - self.betas
						UpperCAmelCase__:  Optional[int]		       =   torch.cumprod(self.alphas							,			dim=0	)

						UpperCAmelCase__:  Union[str, Any]		       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						UpperCAmelCase__:  int		       =   np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
						UpperCAmelCase__:  Tuple		       =   torch.from_numpy(lowerCamelCase__	)

						# setable values
						UpperCAmelCase__:  Optional[int]		       =   None
						UpperCAmelCase__:  Tuple		       =   np.linspace(0							,			num_train_timesteps - 1							,			lowerCamelCase__							,			dtype=lowerCamelCase__	)[::-1].copy()
						UpperCAmelCase__:  Optional[int]		       =   torch.from_numpy(lowerCamelCase__	)
						UpperCAmelCase__:  Union[str, Any]		       =   False





					@property
					def       _UpperCAmelCase						(							self	):
						# standard deviation of the initial noise distribution
						if self.config.timestep_spacing in ["linspace", "trailing"]:
							return self.sigmas.max()

						return (self.sigmas.max() ** 2 + 1) ** 0.5





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__	):
						if isinstance(lowerCamelCase__							,			torch.Tensor	):
							UpperCAmelCase__:  Dict		       =   timestep.to(self.timesteps.device	)
						UpperCAmelCase__:  int		       =   (self.timesteps == timestep).nonzero().item()
						UpperCAmelCase__:  Union[str, Any]		       =   self.sigmas[step_index]
						UpperCAmelCase__:  str		       =   sample / ((sigma**2 + 1) ** 0.5)
						UpperCAmelCase__:  Union[str, Any]		       =   True
						return sample





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__ = None	):
						UpperCAmelCase__:  Union[str, Any]		       =   num_inference_steps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
							UpperCAmelCase__:  int		       =   np.linspace(0							,			self.config.num_train_timesteps - 1							,			lowerCamelCase__							,			dtype=lowerCamelCase__	)[
							    ::-1
							].copy()
						elif self.config.timestep_spacing == "leading":
							UpperCAmelCase__:  int		       =   self.config.num_train_timesteps // self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							UpperCAmelCase__:  List[str]		       =   (np.arange(0							,			lowerCamelCase__	) * step_ratio).round()[::-1].copy().astype(lowerCamelCase__	)
							timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
							UpperCAmelCase__:  Union[str, Any]		       =   self.config.num_train_timesteps / self.num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# casting to int to avoid issues when num_inference_step is power of 3
							UpperCAmelCase__:  Tuple		       =   (np.arange(self.config.num_train_timesteps							,			0							,			-step_ratio	)).round().copy().astype(lowerCamelCase__	)
							timesteps -= 1
						else:
							raise ValueError(
							    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

						UpperCAmelCase__:  Tuple		       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
						UpperCAmelCase__:  Any		       =   np.interp(lowerCamelCase__							,			np.arange(0							,			len(lowerCamelCase__	)	)							,			lowerCamelCase__	)
						UpperCAmelCase__:  Any		       =   np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
						UpperCAmelCase__:  Union[str, Any]		       =   torch.from_numpy(lowerCamelCase__	).to(device=lowerCamelCase__	)
						if str(lowerCamelCase__	).startswith("mps"	):
							# mps does not support float64
							UpperCAmelCase__:  List[Any]		       =   torch.from_numpy(lowerCamelCase__	).to(lowerCamelCase__							,			dtype=torch.floataa	)
						else:
							UpperCAmelCase__:  Union[str, Any]		       =   torch.from_numpy(lowerCamelCase__	).to(device=lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = None							,			lowerCamelCase__ = True							,			):

						if (
						    isinstance(lowerCamelCase__							,			lowerCamelCase__	)
						    or isinstance(lowerCamelCase__							,			torch.IntTensor	)
						    or isinstance(lowerCamelCase__							,			torch.LongTensor	)
						):
							raise ValueError(
							    (
							        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
							        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
							        " one of the `scheduler.timesteps` as a timestep."
							    )							,			)

						if not self.is_scale_input_called:
							logger.warning(
							    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
							    "See `StableDiffusionPipeline` for a usage example."	)

						if isinstance(lowerCamelCase__							,			torch.Tensor	):
							UpperCAmelCase__:  Union[str, Any]		       =   timestep.to(self.timesteps.device	)

						UpperCAmelCase__:  Union[str, Any]		       =   (self.timesteps == timestep).nonzero().item()
						UpperCAmelCase__:  Tuple		       =   self.sigmas[step_index]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
							UpperCAmelCase__:  Tuple		       =   sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
							# * c_out + input * c_skip
							UpperCAmelCase__:  Any		       =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						elif self.config.prediction_type == "sample":
							raise NotImplementedError("prediction_type not implemented yet: sample"	)
						else:
							raise ValueError(
							    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)

						UpperCAmelCase__:  Any		       =   self.sigmas[step_index]
						UpperCAmelCase__:  Optional[int]		       =   self.sigmas[step_index + 1]
						UpperCAmelCase__:  Union[str, Any]		       =   (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
						UpperCAmelCase__:  List[str]		       =   (sigma_to**2 - sigma_up**2) ** 0.5

						# 2. Convert to an ODE derivative
						UpperCAmelCase__:  Union[str, Any]		       =   (sample - pred_original_sample) / sigma

						UpperCAmelCase__:  Union[str, Any]		       =   sigma_down - sigma

						UpperCAmelCase__:  List[Any]		       =   sample + derivative * dt

						UpperCAmelCase__:  Tuple		       =   model_output.device
						UpperCAmelCase__:  List[Any]		       =   randn_tensor(model_output.shape							,			dtype=model_output.dtype							,			device=lowerCamelCase__							,			generator=lowerCamelCase__	)

						UpperCAmelCase__:  Tuple		       =   prev_sample + noise * sigma_up

						if not return_dict:
							return (prev_sample,)

						return EulerAncestralDiscreteSchedulerOutput(
						    prev_sample=lowerCamelCase__							,			pred_original_sample=lowerCamelCase__	)





					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			):
						# Make sure sigmas and timesteps have the same device and dtype as original_samples
						UpperCAmelCase__:  Optional[Any]		       =   self.sigmas.to(device=original_samples.device							,			dtype=original_samples.dtype	)
						if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase__	):
							# mps does not support float64
							UpperCAmelCase__:  Any		       =   self.timesteps.to(original_samples.device							,			dtype=torch.floataa	)
							UpperCAmelCase__:  int		       =   timesteps.to(original_samples.device							,			dtype=torch.floataa	)
						else:
							UpperCAmelCase__:  Union[str, Any]		       =   self.timesteps.to(original_samples.device	)
							UpperCAmelCase__:  Union[str, Any]		       =   timesteps.to(original_samples.device	)

						UpperCAmelCase__:  List[Any]		       =   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

						UpperCAmelCase__:  List[str]		       =   sigmas[step_indices].flatten()
						while len(sigma.shape	) < len(original_samples.shape	):
							UpperCAmelCase__:  Union[str, Any]		       =   sigma.unsqueeze(-1	)

						UpperCAmelCase__:  Union[str, Any]		       =   original_samples + noise * sigma
						return noisy_samples





					def __len__(							self	):
						return self.config.num_train_timesteps


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 __UpperCamelCase    (							unittest.TestCase		):






					'''simple docstring'''



					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()

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

						UpperCAmelCase__:  Tuple		       =   {
						    "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,
						}

						UpperCAmelCase__:  Tuple		       =   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	):
						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]

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

						return image_inputs





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  int		       =   self.get_tokenizer()
						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()

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

						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
						processor_fast.save_pretrained(self.tmpdirname	)
						UpperCAmelCase__:  int		       =   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	):
						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
						processor.save_pretrained(self.tmpdirname	)

						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)

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

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

						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
						UpperCAmelCase__:  str		       =   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	)
						UpperCAmelCase__:  Optional[int]		       =   image_processor(
						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
						random.seed(1_2_3_4	)
						UpperCAmelCase__:  Tuple		       =   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	):
						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
						UpperCAmelCase__:  str		       =   self.get_tokenizer()

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

						UpperCAmelCase__:  Union[str, Any]		       =   "lower newer"

						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)

						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)

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





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  int		       =   self.get_tokenizer()

						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  Any		       =   "lower newer"
						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()

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

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

						# add extra args
						UpperCAmelCase__:  str		       =   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	):
						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()

						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)

						UpperCAmelCase__:  List[Any]		       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)

						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)





					def       _UpperCAmelCase						(							self	):
						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()

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

						UpperCAmelCase__:  Optional[Any]		       =   "lower newer"
						UpperCAmelCase__:  Dict		       =   self.prepare_image_inputs()

						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)

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