



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

_lowercase     							=						'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''






def      _A				(UpperCamelCase		:      List[Any]      ,			UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Any=8 )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		h // scale_factor**2
			if h % scale_factor**2 != 0:
						new_h += 1
			lowerCamelCase__ :      Union[str, Any]								=		w // scale_factor**2
			if w % scale_factor**2 != 0:
						new_w += 1
			return new_h * scale_factor, new_w * scale_factor
class 				__A					( A_      ):





			def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    ):
						super().__init__()

						self.register_modules(
						    text_encoder=__magic_name__							,    tokenizer=__magic_name__							,    unet=__magic_name__							,    scheduler=__magic_name__							,    movq=__magic_name__							,    )
						lowerCamelCase__ :      int								=		2 ** (len(self.movq.config.block_out_channels				) - 1)





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						if latents is None:
									lowerCamelCase__ :      Tuple								=		randn_tensor(__magic_name__							,    generator=__magic_name__							,    device=__magic_name__							,    dtype=__magic_name__				)
						else:
									if latents.shape != shape:
												raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"				)
									lowerCamelCase__ :      Dict								=		latents.to(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		latents * scheduler.init_noise_sigma
						return latents





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      Optional[Any]								=		len(__magic_name__				) if isinstance(__magic_name__							,    __magic_name__				) else 1
						# get prompt text embeddings
						lowerCamelCase__ :      Optional[Any]								=		self.tokenizer(
						    __magic_name__							,    padding="""max_length"""							,    truncation=__magic_name__							,    max_length=77							,    return_attention_mask=__magic_name__							,    add_special_tokens=__magic_name__							,    return_tensors="""pt"""							,    )

						lowerCamelCase__ :      Any								=		text_inputs.input_ids
						lowerCamelCase__ :      Optional[Any]								=		self.tokenizer(__magic_name__							,    padding="""longest"""							,    return_tensors="""pt"""				).input_ids

						if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Union[str, Any]								=		self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    f" {self.tokenizer.model_max_length} tokens: {removed_text}"				)

						lowerCamelCase__ :      List[str]								=		text_input_ids.to(__magic_name__				)
						lowerCamelCase__ :      Tuple								=		text_inputs.attention_mask.to(__magic_name__				)

						lowerCamelCase__							,lowerCamelCase__ :      str								=		self.text_encoder(
						    input_ids=__magic_name__							,    attention_mask=__magic_name__				)

						lowerCamelCase__ :      int								=		prompt_embeds.repeat_interleave(__magic_name__							,    dim=0				)
						lowerCamelCase__ :      Dict								=		text_encoder_hidden_states.repeat_interleave(__magic_name__							,    dim=0				)
						lowerCamelCase__ :      Any								=		text_mask.repeat_interleave(__magic_name__							,    dim=0				)

						if do_classifier_free_guidance:
									lowerCamelCase__ :      List[str]
									if negative_prompt is None:
												lowerCamelCase__ :      Any								=		[""""""] * batch_size
									elif type(__magic_name__				) is not type(__magic_name__				):
												raise TypeError(
												    f"`negative_prompt` should be the same type to `prompt`, but got {type(__magic_name__				)} !="
												    f" {type(__magic_name__				)}."				)
									elif isinstance(__magic_name__							,    __magic_name__				):
												lowerCamelCase__ :      Optional[Any]								=		[negative_prompt]
									elif batch_size != len(__magic_name__				):
												raise ValueError(
												    f"`negative_prompt`: {negative_prompt} has batch size {len(__magic_name__				)}, but `prompt`:"
												    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
												    """ the batch size of `prompt`."""				)
									else:
												lowerCamelCase__ :      Any								=		negative_prompt

									lowerCamelCase__ :      Dict								=		self.tokenizer(
									    __magic_name__							,    padding="""max_length"""							,    max_length=77							,    truncation=__magic_name__							,    return_attention_mask=__magic_name__							,    add_special_tokens=__magic_name__							,    return_tensors="""pt"""							,    )
									lowerCamelCase__ :      Optional[Any]								=		uncond_input.input_ids.to(__magic_name__				)
									lowerCamelCase__ :      Optional[int]								=		uncond_input.attention_mask.to(__magic_name__				)

									lowerCamelCase__							,lowerCamelCase__ :      Dict								=		self.text_encoder(
									    input_ids=__magic_name__							,    attention_mask=__magic_name__				)

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									lowerCamelCase__ :      str								=		negative_prompt_embeds.shape[1]
									lowerCamelCase__ :      Union[str, Any]								=		negative_prompt_embeds.repeat(1							,    __magic_name__				)
									lowerCamelCase__ :      Any								=		negative_prompt_embeds.view(batch_size * num_images_per_prompt							,    __magic_name__				)

									lowerCamelCase__ :      Optional[int]								=		uncond_text_encoder_hidden_states.shape[1]
									lowerCamelCase__ :      Optional[int]								=		uncond_text_encoder_hidden_states.repeat(1							,    __magic_name__							,    1				)
									lowerCamelCase__ :      Optional[int]								=		uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt							,    __magic_name__							,    -1				)
									lowerCamelCase__ :      Optional[int]								=		uncond_text_mask.repeat_interleave(__magic_name__							,    dim=0				)

									# done duplicates

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									lowerCamelCase__ :      List[Any]								=		torch.cat([negative_prompt_embeds, prompt_embeds]				)
									lowerCamelCase__ :      List[str]								=		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

									lowerCamelCase__ :      List[str]								=		torch.cat([uncond_text_mask, text_mask]				)

						return prompt_embeds, text_encoder_hidden_states, text_mask





			def        _snake_case    (self							,    __magic_name__=0				):
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

						lowerCamelCase__ :      Optional[Any]								=		torch.device(f"cuda:{gpu_id}"				)

						lowerCamelCase__ :      int								=		[
						    self.unet,
						    self.text_encoder,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self							,    __magic_name__=0				):
						if is_accelerate_available() and is_accelerate_version(""">="""							,    """0.17.0.dev0"""				):
									from accelerate import cpu_offload_with_hook
						else:
									raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""				)

						lowerCamelCase__ :      int								=		torch.device(f"cuda:{gpu_id}"				)

						if self.device.type != "cpu":
									self.to("""cpu"""							,    silence_dtype_warnings=__magic_name__				)
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						lowerCamelCase__ :      Tuple								=		None
						for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									lowerCamelCase__							,lowerCamelCase__ :      Any								=		cpu_offload_with_hook(__magic_name__							,    __magic_name__							,    prev_module_hook=__magic_name__				)

						if self.safety_checker is not None:
									lowerCamelCase__							,lowerCamelCase__ :      Optional[int]								=		cpu_offload_with_hook(self.safety_checker							,    __magic_name__							,    prev_module_hook=__magic_name__				)

						# We'll offload the last model manually.
						lowerCamelCase__ :      Dict								=		hook





			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def        _snake_case    (self				):
						if not hasattr(self.unet							,    """_hf_hook"""				):
									return self.device
						for module in self.unet.modules():
									if (
									    hasattr(__magic_name__							,    """_hf_hook"""				)
									    and hasattr(module._hf_hook							,    """execution_device"""				)
									    and module._hf_hook.execution_device is not None
									):
												return torch.device(module._hf_hook.execution_device				)
						return self.device





			@torch.no_grad()
			@replace_example_docstring(__magic_name__				)
			def __call__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__ = 512							,    __magic_name__ = 512							,    __magic_name__ = 100							,    __magic_name__ = 4.0							,    __magic_name__ = 1							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = "pil"							,    __magic_name__ = True							,    ):

						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      int								=		1
						elif isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Optional[int]								=		len(__magic_name__				)
						else:
									raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__magic_name__				)}"				)

						lowerCamelCase__ :      str								=		self._execution_device

						lowerCamelCase__ :      Optional[int]								=		batch_size * num_images_per_prompt
						lowerCamelCase__ :      Union[str, Any]								=		guidance_scale > 1.0

						lowerCamelCase__							,lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		self._encode_prompt(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Tuple								=		torch.cat(__magic_name__							,    dim=0				)
						if isinstance(__magic_name__							,    __magic_name__				):
									lowerCamelCase__ :      Any								=		torch.cat(__magic_name__							,    dim=0				)

						if do_classifier_free_guidance:
									lowerCamelCase__ :      Union[str, Any]								=		image_embeds.repeat_interleave(__magic_name__							,    dim=0				)
									lowerCamelCase__ :      Optional[Any]								=		negative_image_embeds.repeat_interleave(__magic_name__							,    dim=0				)

						lowerCamelCase__ :      Dict								=		torch.cat([negative_image_embeds, image_embeds]							,    dim=0				).to(
						    dtype=prompt_embeds.dtype							,    device=__magic_name__				)

						self.scheduler.set_timesteps(__magic_name__							,    device=__magic_name__				)
						lowerCamelCase__ :      Dict								=		self.scheduler.timesteps

						lowerCamelCase__ :      Union[str, Any]								=		self.unet.config.in_channels

						lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		get_new_h_w(__magic_name__							,    __magic_name__							,    self.movq_scale_factor				)

						# create initial latent
						lowerCamelCase__ :      Tuple								=		self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)							,    text_encoder_hidden_states.dtype							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    self.scheduler							,    )

						for i, t in enumerate(self.progress_bar(__magic_name__				)				):
									# expand the latents if we are doing classifier free guidance
									lowerCamelCase__ :      Dict								=		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

									lowerCamelCase__ :      Union[str, Any]								=		{"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
									lowerCamelCase__ :      Union[str, Any]								=		self.unet(
									    sample=__magic_name__							,    timestep=__magic_name__							,    encoder_hidden_states=__magic_name__							,    added_cond_kwargs=__magic_name__							,    return_dict=__magic_name__							,    )[0]

									if do_classifier_free_guidance:
												lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		noise_pred.split(latents.shape[1]							,    dim=1				)
												lowerCamelCase__							,lowerCamelCase__ :      str								=		noise_pred.chunk(2				)
												lowerCamelCase__							,lowerCamelCase__ :      Union[str, Any]								=		variance_pred.chunk(2				)
												lowerCamelCase__ :      Optional[int]								=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
												lowerCamelCase__ :      int								=		torch.cat([noise_pred, variance_pred_text]							,    dim=1				)

									if not (
									    hasattr(self.scheduler.config							,    """variance_type"""				)
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
												lowerCamelCase__							,lowerCamelCase__ :      Tuple								=		noise_pred.split(latents.shape[1]							,    dim=1				)

									# compute the previous noisy sample x_t -> x_t-1
									lowerCamelCase__ :      Dict								=		self.scheduler.step(
									    __magic_name__							,    __magic_name__							,    __magic_name__							,    generator=__magic_name__							,    ).prev_sample
						# post-processing
						lowerCamelCase__ :      List[str]								=		self.movq.decode(__magic_name__							,    force_not_quantize=__magic_name__				)["""sample"""]

						if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"				)

						if output_type in ["np", "pil"]:
									lowerCamelCase__ :      Optional[int]								=		image * 0.5 + 0.5
									lowerCamelCase__ :      Any								=		image.clamp(0							,    1				)
									lowerCamelCase__ :      List[Any]								=		image.cpu().permute(0							,    2							,    3							,    1				).float().numpy()

						if output_type == "pil":
									lowerCamelCase__ :      Any								=		self.numpy_to_pil(__magic_name__				)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__magic_name__				)








import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				_lowercase     							=						False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor
class 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=7							,    __magic_name__=3							,    __magic_name__=18							,    __magic_name__=30							,    __magic_name__=400							,    __magic_name__=None							,    __magic_name__=True							,    __magic_name__=True							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      Optional[Any]								=		size if size is not None else {"""height""": 20, """width""": 20}
						lowerCamelCase__ :      List[str]								=		parent
						lowerCamelCase__ :      str								=		batch_size
						lowerCamelCase__ :      List[str]								=		num_channels
						lowerCamelCase__ :      Optional[int]								=		image_size
						lowerCamelCase__ :      Union[str, Any]								=		min_resolution
						lowerCamelCase__ :      List[Any]								=		max_resolution
						lowerCamelCase__ :      Optional[int]								=		size
						lowerCamelCase__ :      List[str]								=		do_normalize
						lowerCamelCase__ :      str								=		do_convert_rgb
						lowerCamelCase__ :      str								=		[512, 1024, 2048, 4096]
						lowerCamelCase__ :      Any								=		patch_size if patch_size is not None else {"""height""": 16, """width""": 16}





			def        _snake_case    (self				):
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
						lowerCamelCase__ :      int								=		Image.open(requests.get(__magic_name__							,    stream=__magic_name__				).raw				).convert("""RGB"""				)
						return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Union[str, Any]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		PixaStructImageProcessingTester(self				)





			@property
			def        _snake_case    (self				):
						return self.image_processor_tester.prepare_image_processor_dict()





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.image_processor_tester.prepare_dummy_image()

						lowerCamelCase__ :      Tuple								=		self.image_processing_class(**self.image_processor_dict				)
						lowerCamelCase__ :      List[str]								=		2048

						lowerCamelCase__ :      Any								=		image_processor(__magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,    torch.tensor(0.06_06				)							,    atol=1E-3							,    rtol=1E-3				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[Any]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      Optional[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[str]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						lowerCamelCase__ :      List[str]								=		True

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(__magic_name__				):
												lowerCamelCase__ :      Tuple								=		image_processor(
												    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches

									lowerCamelCase__ :      Optional[Any]								=		"""Hello"""

									lowerCamelCase__ :      Dict								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Tuple								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Any								=		self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    numpify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    np.ndarray				)

						lowerCamelCase__ :      Optional[int]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      List[str]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      List[str]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Optional[int]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    torchify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    torch.Tensor				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Tuple								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Optional[int]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		PixaStructImageProcessingTester(self							,    num_channels=4				)
						lowerCamelCase__ :      Optional[int]								=		3





			@property
			def        _snake_case    (self				):
						return self.image_processor_tester.prepare_image_processor_dict()





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      str								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      List[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      str								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )



