






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()


class      lowercase__     (			unittest.TestCase	):

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

	@property
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						3
							lowerCAmelCase__       =						(32, 32)

							lowerCAmelCase__       =						floats_tensor((batch_size, num_channels) + sizes		,    rng=random.Random(0       )       ).to(lowercase_       )
							return image

	@property
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						UNetaDConditionModel(
							    block_out_channels=(32, 32, 64)		,    layers_per_block=2		,    sample_size=32		,    in_channels=7		,    out_channels=4		,    down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")		,    up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")		,    cross_attention_dim=32		,    attention_head_dim=8		,    use_linear_projection=lowercase_		,    only_cross_attention=(True, True, False)		,    num_class_embeds=100		,    )
							return model

	@property
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						AutoencoderKL(
							    block_out_channels=[32, 32, 64]		,    in_channels=3		,    out_channels=3		,    down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]		,    up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]		,    latent_channels=4		,    )
							return model

	@property
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							torch.manual_seed(0       )
							lowerCAmelCase__       =						CLIPTextConfig(
							    bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1E-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=1000		,    hidden_act="gelu"		,    projection_dim=512		,    )
							return CLIPTextModel(lowercase_       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__       =						self.dummy_cond_unet_upscale
							lowerCAmelCase__       =						DDPMScheduler()
							lowerCAmelCase__       =						DDIMScheduler(prediction_type="v_prediction"       )
							lowerCAmelCase__       =						self.dummy_vae
							lowerCAmelCase__       =						self.dummy_text_encoder
							lowerCAmelCase__       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

							lowerCAmelCase__       =						self.dummy_image.cpu().permute(0		,    2		,    3		,    1       )[0]
							lowerCAmelCase__       =						Image.fromarray(np.uinta(lowercase_       )       ).convert("RGB"       ).resize((64, 64)       )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline(
							    unet=lowercase_		,    low_res_scheduler=lowercase_		,    scheduler=lowercase_		,    vae=lowercase_		,    text_encoder=lowercase_		,    tokenizer=lowercase_		,    max_noise_level=350		,    )
							lowerCAmelCase__       =						sd_pipe.to(lowercase_       )
							sd_pipe.set_progress_bar_config(disable=lowercase_       )

							lowerCAmelCase__       =						"A painting of a squirrel eating a burger"
							lowerCAmelCase__       =						torch.Generator(device=lowercase_       ).manual_seed(0       )
							lowerCAmelCase__       =						sd_pipe(
							    [prompt]		,    image=lowercase_		,    generator=lowercase_		,    guidance_scale=6.0		,    noise_level=20		,    num_inference_steps=2		,    output_type="np"		,    )

							lowerCAmelCase__       =						output.images

							lowerCAmelCase__       =						torch.Generator(device=lowercase_       ).manual_seed(0       )
							lowerCAmelCase__       =						sd_pipe(
							    [prompt]		,    image=lowercase_		,    generator=lowercase_		,    guidance_scale=6.0		,    noise_level=20		,    num_inference_steps=2		,    output_type="np"		,    return_dict=lowercase_		,    )[0]

							lowerCAmelCase__       =						image[0, -3:, -3:, -1]
							lowerCAmelCase__       =						image_from_tuple[0, -3:, -3:, -1]

							lowerCAmelCase__       =						low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)
							lowerCAmelCase__       =						np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]       )

							assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase__       =						self.dummy_cond_unet_upscale
							lowerCAmelCase__       =						DDPMScheduler()
							lowerCAmelCase__       =						DDIMScheduler(prediction_type="v_prediction"       )
							lowerCAmelCase__       =						self.dummy_vae
							lowerCAmelCase__       =						self.dummy_text_encoder
							lowerCAmelCase__       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

							lowerCAmelCase__       =						self.dummy_image.cpu().permute(0		,    2		,    3		,    1       )[0]
							lowerCAmelCase__       =						Image.fromarray(np.uinta(lowercase_       )       ).convert("RGB"       ).resize((64, 64)       )

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline(
							    unet=lowercase_		,    low_res_scheduler=lowercase_		,    scheduler=lowercase_		,    vae=lowercase_		,    text_encoder=lowercase_		,    tokenizer=lowercase_		,    max_noise_level=350		,    )
							lowerCAmelCase__       =						sd_pipe.to(lowercase_       )
							sd_pipe.set_progress_bar_config(disable=lowercase_       )

							lowerCAmelCase__       =						"A painting of a squirrel eating a burger"
							lowerCAmelCase__       =						sd_pipe(
							    2 * [prompt]		,    image=2 * [low_res_image]		,    guidance_scale=6.0		,    noise_level=20		,    num_inference_steps=2		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images
							assert image.shape[0] == 2

							lowerCAmelCase__       =						torch.Generator(device=lowercase_       ).manual_seed(0       )
							lowerCAmelCase__       =						sd_pipe(
							    [prompt]		,    image=lowercase_		,    generator=lowercase_		,    num_images_per_prompt=2		,    guidance_scale=6.0		,    noise_level=20		,    num_inference_steps=2		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images
							assert image.shape[0] == 2


	@unittest.skipIf(torch_device != "cuda"		,    "This test requires a GPU"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						self.dummy_cond_unet_upscale
							lowerCAmelCase__       =						DDPMScheduler()
							lowerCAmelCase__       =						DDIMScheduler(prediction_type="v_prediction"       )
							lowerCAmelCase__       =						self.dummy_vae
							lowerCAmelCase__       =						self.dummy_text_encoder
							lowerCAmelCase__       =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

							lowerCAmelCase__       =						self.dummy_image.cpu().permute(0		,    2		,    3		,    1       )[0]
							lowerCAmelCase__       =						Image.fromarray(np.uinta(lowercase_       )       ).convert("RGB"       ).resize((64, 64)       )

							# put models in fp16, except vae as it overflows in fp16
							lowerCAmelCase__       =						unet.half()
							lowerCAmelCase__       =						text_encoder.half()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline(
							    unet=lowercase_		,    low_res_scheduler=lowercase_		,    scheduler=lowercase_		,    vae=lowercase_		,    text_encoder=lowercase_		,    tokenizer=lowercase_		,    max_noise_level=350		,    )
							lowerCAmelCase__       =						sd_pipe.to(lowercase_       )
							sd_pipe.set_progress_bar_config(disable=lowercase_       )

							lowerCAmelCase__       =						"A painting of a squirrel eating a burger"
							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						sd_pipe(
							    [prompt]		,    image=lowercase_		,    generator=lowercase_		,    num_inference_steps=2		,    output_type="np"		,    ).images

							lowerCAmelCase__       =						low_res_image.size[0] * 4
							assert image.shape == (1, expected_height_width, expected_height_width, 3)





@slow
@require_torch_gpu
class      lowercase__     (			unittest.TestCase	):

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"       )
							lowerCAmelCase__       =						load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
							    "/upsampled_cat.npy"       )

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-x4-upscaler"
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline.from_pretrained(lowercase_       )
							pipe.to(lowercase_       )
							pipe.set_progress_bar_config(disable=lowercase_       )
							pipe.enable_attention_slicing()

							lowerCAmelCase__       =						"a cat sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=lowercase_		,    image=lowercase_		,    generator=lowercase_		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image       ).max() < 1E-3

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"       )
							lowerCAmelCase__       =						load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
							    "/upsampled_cat_fp16.npy"       )

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-x4-upscaler"
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline.from_pretrained(
							    lowercase_		,    torch_dtype=torch.floataa		,    )
							pipe.to(lowercase_       )
							pipe.set_progress_bar_config(disable=lowercase_       )
							pipe.enable_attention_slicing()

							lowerCAmelCase__       =						"a cat sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=lowercase_		,    image=lowercase_		,    generator=lowercase_		,    output_type="np"		,    )
							lowerCAmelCase__       =						output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image       ).max() < 5E-1


	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							lowerCAmelCase__       =						load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/sd2-upscale/low_res_cat.png"       )

							lowerCAmelCase__       =						"stabilityai/stable-diffusion-x4-upscaler"
							lowerCAmelCase__       =						StableDiffusionUpscalePipeline.from_pretrained(
							    lowercase_		,    torch_dtype=torch.floataa		,    )
							pipe.to(lowercase_       )
							pipe.set_progress_bar_config(disable=lowercase_       )
							pipe.enable_attention_slicing(1       )
							pipe.enable_sequential_cpu_offload()

							lowerCAmelCase__       =						"a cat sitting on a park bench"

							lowerCAmelCase__       =						torch.manual_seed(0       )
							lowerCAmelCase__       =						pipe(
							    prompt=lowercase_		,    image=lowercase_		,    generator=lowercase_		,    num_inference_steps=5		,    output_type="np"		,    )

							lowerCAmelCase__       =						torch.cuda.max_memory_allocated()
							# make sure that less than 2.9 GB is allocated
							assert mem_bytes < 2.9 * 10**9












import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 transformers import (
	    MODEL_MAPPING,
	    SegformerForImageClassification,
	    SegformerForSemanticSegmentation,
	    SegformerModel,
	)
	from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor


class      lowercase__     (			_UpperCAmelCase	):

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.config_class(**self.inputs_dict       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "hidden_sizes"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_attention_heads"       )       )
							self.parent.assertTrue(hasattr(__UpperCAmelCase		,    "num_encoder_blocks"       )       )





class      lowercase__     :

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=13		,    __UpperCAmelCase=64		,    __UpperCAmelCase=3		,    __UpperCAmelCase=4		,    __UpperCAmelCase=[2, 2, 2, 2]		,    __UpperCAmelCase=[8, 4, 2, 1]		,    __UpperCAmelCase=[16, 32, 64, 128]		,    __UpperCAmelCase=[1, 4, 8, 16]		,    __UpperCAmelCase=[1, 2, 4, 8]		,    __UpperCAmelCase=True		,    __UpperCAmelCase=True		,    __UpperCAmelCase="gelu"		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.1		,    __UpperCAmelCase=0.02		,    __UpperCAmelCase=3		,    __UpperCAmelCase=None		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						parent
							lowerCAmelCase__       =						batch_size
							lowerCAmelCase__       =						image_size
							lowerCAmelCase__       =						num_channels
							lowerCAmelCase__       =						num_encoder_blocks
							lowerCAmelCase__       =						sr_ratios
							lowerCAmelCase__       =						depths
							lowerCAmelCase__       =						hidden_sizes
							lowerCAmelCase__       =						downsampling_rates
							lowerCAmelCase__       =						num_attention_heads
							lowerCAmelCase__       =						is_training
							lowerCAmelCase__       =						use_labels
							lowerCAmelCase__       =						hidden_act
							lowerCAmelCase__       =						hidden_dropout_prob
							lowerCAmelCase__       =						attention_probs_dropout_prob
							lowerCAmelCase__       =						initializer_range
							lowerCAmelCase__       =						num_labels
							lowerCAmelCase__       =						scope

	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





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

							lowerCAmelCase__       =						None
							if self.use_labels:
													lowerCAmelCase__       =						ids_tensor([self.batch_size, self.image_size, self.image_size]		,    self.num_labels       )

							lowerCAmelCase__       =						self.get_config()
							return config, pixel_values, labels

	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							return SegformerConfig(
							    image_size=self.image_size		,    num_channels=self.num_channels		,    num_encoder_blocks=self.num_encoder_blocks		,    depths=self.depths		,    hidden_sizes=self.hidden_sizes		,    num_attention_heads=self.num_attention_heads		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    initializer_range=self.initializer_range		,    )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModel(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							lowerCAmelCase__       =						lowerCAmelCase__       =						self.image_size // (self.downsampling_rates[-1] * 2)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						self.num_labels
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						model(__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertEqual(
							    result.logits.shape		,    (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
							self.parent.assertGreater(result.loss		,    0.0       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						1
							lowerCAmelCase__       =						SegformerForSemanticSegmentation(config=__UpperCAmelCase       )
							model.to(__UpperCAmelCase       )
							model.eval()
							lowerCAmelCase__       =						torch.randint(0		,    1		,    (self.batch_size, self.image_size, self.image_size)       ).to(__UpperCAmelCase       )
							lowerCAmelCase__       =						model(__UpperCAmelCase		,    labels=__UpperCAmelCase       )
							self.parent.assertGreater(result.loss		,    0.0       )


	def 		UpperCAmelCase  (							self       )->					Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.prepare_config_and_inputs()
							lowerCAmelCase__      ,							lowerCAmelCase__      ,							lowerCAmelCase__       =						config_and_inputs
							lowerCAmelCase__       =						{"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class      lowercase__     (			_UpperCAmelCase,					_UpperCAmelCase,					unittest.TestCase	):
	a_         =(
	    (
	        SegformerModel,
	        SegformerForSemanticSegmentation,
	        SegformerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	a_         =(
	    {
	        """feature-extraction""": SegformerModel,
	        """image-classification""": SegformerForImageClassification,
	        """image-segmentation""": SegformerForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	a_         =True
	a_         =False
	a_         =False
	a_         =False

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerModelTester(self       )
							lowerCAmelCase__       =						SegformerConfigTester(self		,    config_class=__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							self.config_tester.run_common_tests()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase       )

	@unittest.skip("SegFormer does not use inputs_embeds"       )
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							pass

	@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							pass

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCAmelCase__       =						["pixel_values"]
													self.assertListEqual(arg_names[:1]		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					str:







							'''simple docstring'''





							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						False
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													lowerCAmelCase__       =						sum(self.model_tester.depths       )
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )
													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

													# verify the last attentions (last block, last layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 32) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]       )		,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]		,    )
													lowerCAmelCase__       =						len(__UpperCAmelCase       )

													# Check attention is always last and order is fine
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						True
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()
													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													self.assertEqual(out_len + 1		,    len(__UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.attentions

													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )
													# verify the first attentions (first block, first layer)
													lowerCAmelCase__       =						(self.model_tester.image_size // 4) ** 2
													lowerCAmelCase__       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]       )		,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]		,    )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							def check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.eval()

													with torch.no_grad():
																			lowerCAmelCase__       =						model(**self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase       )       )

													lowerCAmelCase__       =						outputs.hidden_states

													lowerCAmelCase__       =						self.model_tester.num_encoder_blocks
													self.assertEqual(len(__UpperCAmelCase       )		,    __UpperCAmelCase       )

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]       )		,    [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]		,    )

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowerCAmelCase__       =						True
													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

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

													check_hidden_states_output(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							if not self.model_tester.is_training:
													return

							lowerCAmelCase__      ,							lowerCAmelCase__       =						self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase__       =						True

							for model_class in self.all_model_classes:
													if model_class in get_values(__UpperCAmelCase       ):
																			continue

													lowerCAmelCase__       =						model_class(__UpperCAmelCase       )
													model.to(__UpperCAmelCase       )
													model.train()
													lowerCAmelCase__       =						self._prepare_for_class(__UpperCAmelCase		,    __UpperCAmelCase		,    return_labels=__UpperCAmelCase       )
													lowerCAmelCase__       =						model(**__UpperCAmelCase       ).loss
													loss.backward()

	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							pass


	@slow
	def 		UpperCAmelCase  (							self       )->					Union[str, Any]:







							'''simple docstring'''





							for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCAmelCase__       =						SegformerModel.from_pretrained(__UpperCAmelCase       )
													self.assertIsNotNone(__UpperCAmelCase       )





def        _a  (     ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image





@require_torch
class      lowercase__     (			unittest.TestCase	):

	@slow
	def 		UpperCAmelCase  (							self       )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
							        [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
							        [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-4       )       )

	@slow
	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained(
							    "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						torch.Size((1, model.config.num_labels, 128, 128)       )
							self.assertEqual(outputs.logits.shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						torch.tensor(
							    [
							        [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
							        [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
							        [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
							    ]       ).to(__UpperCAmelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]		,    __UpperCAmelCase		,    atol=1E-1       )       )


	@slow
	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						SegformerImageProcessor(
							    image_scale=(512, 512)		,    keep_ratio=__UpperCAmelCase		,    align=__UpperCAmelCase		,    do_random_crop=__UpperCAmelCase       )
							lowerCAmelCase__       =						SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
							    __UpperCAmelCase       )

							lowerCAmelCase__       =						prepare_img()
							lowerCAmelCase__       =						image_processor(images=__UpperCAmelCase		,    return_tensors="pt"       )
							lowerCAmelCase__       =						encoded_inputs.pixel_values.to(__UpperCAmelCase       )

							with torch.no_grad():
													lowerCAmelCase__       =						model(__UpperCAmelCase       )

							lowerCAmelCase__       =						outputs.logits.detach().cpu()

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase		,    target_sizes=[(500, 300)]       )
							lowerCAmelCase__       =						torch.Size((500, 300)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase       )
							lowerCAmelCase__       =						torch.Size((128, 128)       )
							self.assertEqual(segmentation[0].shape		,    __UpperCAmelCase       )






