






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__()

							if safety_checker is None:
													logger.warning(
													    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
													    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
													    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
													    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
													    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
													    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'    )

							self.register_modules(
							    speech_model=UpperCAmelCase       ,    speech_processor=UpperCAmelCase       ,    vae=UpperCAmelCase       ,    text_encoder=UpperCAmelCase       ,    tokenizer=UpperCAmelCase       ,    unet=UpperCAmelCase       ,    scheduler=UpperCAmelCase       ,    feature_extractor=UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = "auto"    ):
							"""simple docstring"""







							if slice_size == "auto":
													_UpperCAmelCase									=			self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.enable_attention_slicing(UpperCAmelCase    )

	@torch.no_grad()
	def __call__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1_6000       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 50       ,    UpperCAmelCase = 7.5       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    UpperCAmelCase = 0.0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "pil"       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.speech_processor.feature_extractor(
							    UpperCAmelCase       ,    return_tensors='pt'       ,    sampling_rate=UpperCAmelCase    ).input_features.to(self.device    )
							_UpperCAmelCase									=			self.speech_model.generate(UpperCAmelCase       ,    max_length=48_0000    )

							_UpperCAmelCase									=			self.speech_processor.tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase       ,    normalize=UpperCAmelCase    )[
							    0
							]

							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			1
							elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			len(UpperCAmelCase    )
							else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase    )}"""    )

							if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
													    F""" {type(UpperCAmelCase    )}."""    )

							# get prompt text embeddings
							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    padding='max_length'       ,    max_length=self.tokenizer.model_max_length       ,    return_tensors='pt'       ,    )
							_UpperCAmelCase									=			text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													_UpperCAmelCase									=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
													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}"""    )
													_UpperCAmelCase									=			text_input_ids[:, : self.tokenizer.model_max_length]
							_UpperCAmelCase									=			self.text_encoder(text_input_ids.to(self.device    )    )[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			text_embeddings.shape
							_UpperCAmelCase									=			text_embeddings.repeat(1       ,    UpperCAmelCase       ,    1    )
							_UpperCAmelCase									=			text_embeddings.view(bs_embed * num_images_per_prompt       ,    UpperCAmelCase       ,    -1    )

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCAmelCase									=			guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													_UpperCAmelCase									=			42
													if negative_prompt is None:
																			_UpperCAmelCase									=			[''] * batch_size
													elif type(UpperCAmelCase    ) is not type(UpperCAmelCase    ):
																			raise TypeError(
																			    F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase    )} !="""
																			    F""" {type(UpperCAmelCase    )}."""    )
													elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																			_UpperCAmelCase									=			[negative_prompt]
													elif batch_size != len(UpperCAmelCase    ):
																			raise ValueError(
																			    F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase    )}, but `prompt`:"""
																			    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																			    ' the batch size of `prompt`.'    )
													else:
																			_UpperCAmelCase									=			negative_prompt

													_UpperCAmelCase									=			text_input_ids.shape[-1]
													_UpperCAmelCase									=			self.tokenizer(
													    UpperCAmelCase       ,    padding='max_length'       ,    max_length=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    return_tensors='pt'       ,    )
													_UpperCAmelCase									=			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													_UpperCAmelCase									=			uncond_embeddings.shape[1]
													_UpperCAmelCase									=			uncond_embeddings.repeat(1       ,    UpperCAmelCase       ,    1    )
													_UpperCAmelCase									=			uncond_embeddings.view(batch_size * num_images_per_prompt       ,    UpperCAmelCase       ,    -1    )

													# 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
													_UpperCAmelCase									=			torch.cat([uncond_embeddings, text_embeddings]    )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCAmelCase									=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCAmelCase									=			text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not exist on mps
																			_UpperCAmelCase									=			torch.randn(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    device='cpu'       ,    dtype=UpperCAmelCase    ).to(
																			    self.device    )
													else:
																			_UpperCAmelCase									=			torch.randn(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    device=self.device       ,    dtype=UpperCAmelCase    )
							else:
													if latents.shape != latents_shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
													_UpperCAmelCase									=			latents.to(self.device    )

							# set timesteps
							self.scheduler.set_timesteps(UpperCAmelCase    )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCAmelCase									=			self.scheduler.timesteps.to(self.device    )

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCAmelCase									=			latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCAmelCase									=			'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
							_UpperCAmelCase									=			{}
							if accepts_eta:
													_UpperCAmelCase									=			eta

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

													# predict the noise residual
													_UpperCAmelCase									=			self.unet(UpperCAmelCase       ,    UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase    ).sample

													# perform guidance
													if do_classifier_free_guidance:
																			_UpperCAmelCase					, _UpperCAmelCase									=			noise_pred.chunk(2    )
																			_UpperCAmelCase									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													_UpperCAmelCase									=			self.scheduler.step(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																			callback(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			1 / 0.1_82_15 * latents
							_UpperCAmelCase									=			self.vae.decode(UpperCAmelCase    ).sample

							_UpperCAmelCase									=			(image / 2 + 0.5).clamp(0       ,    1    )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    ).float().numpy()

							if output_type == "pil":
													_UpperCAmelCase									=			self.numpy_to_pil(UpperCAmelCase    )

							if not return_dict:
													return image

							return StableDiffusionPipelineOutput(images=UpperCAmelCase       ,    nsfw_content_detected=UpperCAmelCase    )








import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MaskFormerSwinBackbone
		from transformers.models.maskformer import MaskFormerSwinModel





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=13       ,    UpperCAmelCase=32       ,    UpperCAmelCase=2       ,    UpperCAmelCase=3       ,    UpperCAmelCase=16       ,    UpperCAmelCase=[1, 2, 1]       ,    UpperCAmelCase=[2, 2, 4]       ,    UpperCAmelCase=2       ,    UpperCAmelCase=2.0       ,    UpperCAmelCase=True       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=1e-5       ,    UpperCAmelCase=True       ,    UpperCAmelCase=None       ,    UpperCAmelCase=True       ,    UpperCAmelCase=10       ,    UpperCAmelCase=8       ,    UpperCAmelCase=["stage1", "stage2", "stage3"]       ,    UpperCAmelCase=[1, 2, 3]       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			image_size
							_UpperCAmelCase									=			patch_size
							_UpperCAmelCase									=			num_channels
							_UpperCAmelCase									=			embed_dim
							_UpperCAmelCase									=			depths
							_UpperCAmelCase									=			num_heads
							_UpperCAmelCase									=			window_size
							_UpperCAmelCase									=			mlp_ratio
							_UpperCAmelCase									=			qkv_bias
							_UpperCAmelCase									=			hidden_dropout_prob
							_UpperCAmelCase									=			attention_probs_dropout_prob
							_UpperCAmelCase									=			drop_path_rate
							_UpperCAmelCase									=			hidden_act
							_UpperCAmelCase									=			use_absolute_embeddings
							_UpperCAmelCase									=			patch_norm
							_UpperCAmelCase									=			layer_norm_eps
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			scope
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			type_sequence_label_size
							_UpperCAmelCase									=			encoder_stride
							_UpperCAmelCase									=			out_features
							_UpperCAmelCase									=			out_indices

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

							_UpperCAmelCase									=			None
							if self.use_labels:
													_UpperCAmelCase									=			ids_tensor([self.batch_size]       ,    self.type_sequence_label_size    )

							_UpperCAmelCase									=			self.get_config()

							return config, pixel_values, labels

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return MaskFormerSwinConfig(
							    image_size=self.image_size       ,    patch_size=self.patch_size       ,    num_channels=self.num_channels       ,    embed_dim=self.embed_dim       ,    depths=self.depths       ,    num_heads=self.num_heads       ,    window_size=self.window_size       ,    mlp_ratio=self.mlp_ratio       ,    qkv_bias=self.qkv_bias       ,    hidden_dropout_prob=self.hidden_dropout_prob       ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,    drop_path_rate=self.drop_path_rate       ,    hidden_act=self.hidden_act       ,    use_absolute_embeddings=self.use_absolute_embeddings       ,    path_norm=self.patch_norm       ,    layer_norm_eps=self.layer_norm_eps       ,    initializer_range=self.initializer_range       ,    encoder_stride=self.encoder_stride       ,    out_features=self.out_features       ,    out_indices=self.out_indices       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModel(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )

							_UpperCAmelCase									=			((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
							_UpperCAmelCase									=			int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

							self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, expected_seq_len, expected_dim)    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinBackbone(config=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()
							_UpperCAmelCase									=			model(UpperCAmelCase    )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps    )       ,    len(config.out_features    )    )
							self.parent.assertListEqual(list(result.feature_maps[0].shape    )       ,    [13, 16, 16, 16]    )

							# verify channels
							self.parent.assertEqual(len(model.channels    )       ,    len(config.out_features    )    )
							self.parent.assertListEqual(model.channels       ,    [16, 32, 64]    )

							# verify ValueError
							with self.parent.assertRaises(UpperCAmelCase    ):
													_UpperCAmelCase									=			['stem']
													_UpperCAmelCase									=			MaskFormerSwinBackbone(config=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			config_and_inputs
							_UpperCAmelCase									=			{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (
	    (
	        MaskFormerSwinModel,
	        MaskFormerSwinBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase__								=      {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    embed_dim=37    )

	@require_torch_multi_gpu
	@unittest.skip(
	    reason=(
	        '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
	        ' `nn.DataParallel`'
	    )    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCAmelCase    )

	@unittest.skip('Swin does not use inputs_embeds'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip('Swin does not support feedforward chunking'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													self.assertIsInstance(model.get_input_embeddings()       ,    (nn.Module)    )
													_UpperCAmelCase									=			model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(UpperCAmelCase       ,    nn.Linear    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													_UpperCAmelCase									=			inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													_UpperCAmelCase									=			[*signature.parameters.keys()]

													_UpperCAmelCase									=			['pixel_values']
													self.assertListEqual(arg_names[:1]       ,    UpperCAmelCase    )

	@unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			model_class(UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							model.eval()

							with torch.no_grad():
													_UpperCAmelCase									=			model(**self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )    )

							_UpperCAmelCase									=			outputs.hidden_states

							_UpperCAmelCase									=			getattr(
							    self.model_tester       ,    'expected_num_hidden_layers'       ,    len(self.model_tester.depths    ) + 1    )
							self.assertEqual(len(UpperCAmelCase    )       ,    UpperCAmelCase    )

							# Swin has a different seq_length
							_UpperCAmelCase									=			(
							    config.patch_size
							    if isinstance(config.patch_size       ,    collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							_UpperCAmelCase									=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]    )       ,    [num_patches, self.model_tester.embed_dim]       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCAmelCase									=			(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_UpperCAmelCase									=			True

													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			3

							_UpperCAmelCase									=			(
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size       ,    collections.abc.Iterable    )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							_UpperCAmelCase									=			(
							    config.patch_size
							    if isinstance(config.patch_size       ,    collections.abc.Iterable    )
							    else (config.patch_size, config.patch_size)
							)

							_UpperCAmelCase									=			image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							_UpperCAmelCase									=			image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    (padded_height, padded_width)    )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													_UpperCAmelCase									=			True
													self.check_hidden_states_output(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    (padded_height, padded_width)    )

	@unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	@unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()

							def set_nan_tensor_to_zero(UpperCAmelCase    ):
													_UpperCAmelCase									=			0
													return t

							def check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase={}    ):
													with torch.no_grad():
																			_UpperCAmelCase									=			model(**UpperCAmelCase       ,    return_dict=UpperCAmelCase       ,    **UpperCAmelCase    )
																			_UpperCAmelCase									=			model(**UpperCAmelCase       ,    return_dict=UpperCAmelCase       ,    **UpperCAmelCase    ).to_tuple()

																			def recursive_check(UpperCAmelCase       ,    UpperCAmelCase    ):
																									if isinstance(UpperCAmelCase       ,    (List, Tuple)    ):
																															for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase       ,    UpperCAmelCase    ):
																																					recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )
																									elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																															for tuple_iterable_value, dict_iterable_value in zip(
																															    tuple_object.values()       ,    dict_object.values()    ):
																																					recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )
																									elif tuple_object is None:
																															return
																									else:
																															self.assertTrue(
																															    torch.allclose(
																															        set_nan_tensor_to_zero(UpperCAmelCase    )       ,    set_nan_tensor_to_zero(UpperCAmelCase    )       ,    atol=1e-5    )       ,    msg=(
																															        'Tuple and dict output are not equal. Difference:'
																															        F""" {torch.max(torch.abs(tuple_object - dict_object    )    )}. Tuple has `nan`:"""
																															        F""" {torch.isnan(UpperCAmelCase    ).any()} and `inf`: {torch.isinf(UpperCAmelCase    )}. Dict has"""
																															        F""" `nan`: {torch.isnan(UpperCAmelCase    ).any()} and `inf`: {torch.isinf(UpperCAmelCase    )}."""
																															    )       ,    )

																			recursive_check(UpperCAmelCase       ,    UpperCAmelCase    )

							for model_class in self.all_model_classes:
													_UpperCAmelCase									=			model_class(UpperCAmelCase    )
													model.to(UpperCAmelCase    )
													model.eval()

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    {'output_hidden_states': True}    )

													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													_UpperCAmelCase									=			self._prepare_for_class(UpperCAmelCase       ,    UpperCAmelCase       ,    return_labels=UpperCAmelCase    )
													check_equivalence(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    {'output_hidden_states': True}    )





@require_torch
class __lowerCamelCase (      unittest.TestCase     ,       snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      (MaskFormerSwinBackbone,) if is_torch_available() else ()
	UpperCamelCase__								=      MaskFormerSwinConfig

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			MaskFormerSwinModelTester(self    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCAmelCase									=			inputs_dict['pixel_values'].shape[0]

							for backbone_class in self.all_model_classes:
													_UpperCAmelCase									=			backbone_class(UpperCAmelCase    )
													backbone.to(UpperCAmelCase    )
													backbone.eval()

													_UpperCAmelCase									=			backbone(**UpperCAmelCase    )

													# Test default outputs and verify feature maps
													self.assertIsInstance(outputs.feature_maps       ,    UpperCAmelCase    )
													self.assertTrue(len(outputs.feature_maps    ) == len(backbone.channels    )    )
													for feature_map, n_channels in zip(outputs.feature_maps       ,    backbone.channels    ):
																			self.assertTrue(feature_map.shape[:2]       ,    (batch_size, n_channels)    )
													self.assertIsNone(outputs.hidden_states    )
													self.assertIsNone(outputs.attentions    )

													# Test output_hidden_states=True
													_UpperCAmelCase									=			backbone(**UpperCAmelCase       ,    output_hidden_states=UpperCAmelCase    )
													self.assertIsNotNone(outputs.hidden_states    )
													self.assertTrue(len(outputs.hidden_states    )       ,    len(backbone.stage_names    )    )
													# We skip the stem layer
													for hidden_states, n_channels in zip(outputs.hidden_states[1:]       ,    backbone.channels    ):
																			for hidden_state in hidden_states:
																									# Hidden states are in the format (batch_size, (height * width), n_channels)
																									_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			hidden_state.shape
																									self.assertTrue((h_batch_size, h_n_channels)       ,    (batch_size, n_channels)    )

            # Test output_attentions=True
													if self.has_attentions:
																			_UpperCAmelCase									=			backbone(**UpperCAmelCase       ,    output_attentions=UpperCAmelCase    )
																			self.assertIsNotNone(outputs.attentions    )
