





"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				UnCLIPPipeline
					UpperCamelCase						=				TEXT_TO_IMAGE_PARAMS - {
					    '''negative_prompt''',
					    '''height''',
					    '''width''',
					    '''negative_prompt_embeds''',
					    '''guidance_scale''',
					    '''prompt_embeds''',
					    '''cross_attention_kwargs''',
					}
					UpperCamelCase						=				TEXT_TO_IMAGE_BATCH_PARAMS
					UpperCamelCase						=				[
					    '''generator''',
					    '''return_dict''',
					    '''prior_num_inference_steps''',
					    '''decoder_num_inference_steps''',
					    '''super_res_num_inference_steps''',
					]
					UpperCamelCase						=				False


					@property
					def       lowerCamelCase			(   self     :Optional[Any]						):
												return 32


					@property
					def       lowerCamelCase			(   self     :Tuple						):
												return 32


					@property
					def       lowerCamelCase			(   self     :str						):
												return self.time_input_dim


					@property
					def       lowerCamelCase			(   self     :int						):
												return self.time_input_dim * 4


					@property
					def       lowerCamelCase			(   self     :Optional[Any]						):
												return 1_00


					@property
					def       lowerCamelCase			(   self     :List[str]						):
												A						=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
												return tokenizer


					@property
					def       lowerCamelCase			(   self     :List[str]						):
												torch.manual_seed(0						)
												A						=    CLIPTextConfig(
												    bos_token_id=0	,			eos_token_id=2	,			hidden_size=self.text_embedder_hidden_size	,			projection_dim=self.text_embedder_hidden_size	,			intermediate_size=37	,			layer_norm_eps=1e-05	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=10_00	,			)
												return CLIPTextModelWithProjection(__UpperCamelCase						)


					@property
					def       lowerCamelCase			(   self     :int						):
												torch.manual_seed(0						)

												A						=    {
												    "num_attention_heads": 2,
												    "attention_head_dim": 12,
												    "embedding_dim": self.text_embedder_hidden_size,
												    "num_layers": 1,
												}

												A						=    PriorTransformer(**__UpperCamelCase						)
												return model


					@property
					def       lowerCamelCase			(   self     :List[str]						):
												torch.manual_seed(0						)

												A						=    {
												    "clip_embeddings_dim": self.text_embedder_hidden_size,
												    "time_embed_dim": self.time_embed_dim,
												    "cross_attention_dim": self.cross_attention_dim,
												}

												A						=    UnCLIPTextProjModel(**__UpperCamelCase						)
												return model


					@property
					def       lowerCamelCase			(   self     :Optional[int]						):
												torch.manual_seed(0						)

												A						=    {
												    "sample_size": 32,
												    # RGB in channels
												    "in_channels": 3,
												    # Out channels is double in channels because predicts mean and variance
												    "out_channels": 6,
												    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
												    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
												    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "layers_per_block": 1,
												    "cross_attention_dim": self.cross_attention_dim,
												    "attention_head_dim": 4,
												    "resnet_time_scale_shift": "scale_shift",
												    "class_embed_type": "identity",
												}

												A						=    UNetaDConditionModel(**__UpperCamelCase						)
												return model


					@property
					def       lowerCamelCase			(   self     :str						):
												return {
												    "sample_size": 64,
												    "layers_per_block": 1,
												    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
												    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
												    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
												    "in_channels": 6,
												    "out_channels": 3,
												}


					@property
					def       lowerCamelCase			(   self     :Any						):
												torch.manual_seed(0						)

												A						=    UNetaDModel(**self.dummy_super_res_kwargs						)
												return model


					@property
					def       lowerCamelCase			(   self     :Tuple						):
												# seeded differently to get different unet than `self.dummy_super_res_first`
												torch.manual_seed(1						)

												A						=    UNetaDModel(**self.dummy_super_res_kwargs						)
												return model


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.dummy_prior
												A						=    self.dummy_decoder
												A						=    self.dummy_text_proj
												A						=    self.dummy_text_encoder
												A						=    self.dummy_tokenizer
												A						=    self.dummy_super_res_first
												A						=    self.dummy_super_res_last

												A						=    UnCLIPScheduler(
												    variance_type="fixed_small_log"	,			prediction_type="sample"	,			num_train_timesteps=10_00	,			clip_sample_range=5.0	,			)

												A						=    UnCLIPScheduler(
												    variance_type="learned_range"	,			prediction_type="epsilon"	,			num_train_timesteps=10_00	,			)

												A						=    UnCLIPScheduler(
												    variance_type="fixed_small_log"	,			prediction_type="epsilon"	,			num_train_timesteps=10_00	,			)

												A						=    {
												    "prior": prior,
												    "decoder": decoder,
												    "text_proj": text_proj,
												    "text_encoder": text_encoder,
												    "tokenizer": tokenizer,
												    "super_res_first": super_res_first,
												    "super_res_last": super_res_last,
												    "prior_scheduler": prior_scheduler,
												    "decoder_scheduler": decoder_scheduler,
												    "super_res_scheduler": super_res_scheduler,
												}

												return components


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :Optional[int]=0						):
												if str(__UpperCamelCase						).startswith("mps"						):
																			A						=    torch.manual_seed(__UpperCamelCase						)
												else:
																			A						=    torch.Generator(device=__UpperCamelCase						).manual_seed(__UpperCamelCase						)
												A						=    {
												    "prompt": "horse",
												    "generator": generator,
												    "prior_num_inference_steps": 2,
												    "decoder_num_inference_steps": 2,
												    "super_res_num_inference_steps": 2,
												    "output_type": "numpy",
												}
												return inputs


					def       lowerCamelCase			(   self     :str						):
												A						=    "cpu"

												A						=    self.get_dummy_components()

												A						=    self.pipeline_class(**__UpperCamelCase						)
												A						=    pipe.to(__UpperCamelCase						)

												pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												A						=    pipe(**self.get_dummy_inputs(__UpperCamelCase						)						)
												A						=    output.images

												A						=    pipe(
												    **self.get_dummy_inputs(__UpperCamelCase						)	,			return_dict=__UpperCamelCase	,			)[0]

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

												assert image.shape == (1, 64, 64, 3)

												A						=    np.array(
												    [
												        0.9_997,
												        0.9_988,
												        0.0_028,
												        0.9_997,
												        0.9_984,
												        0.9_965,
												        0.0_029,
												        0.9_986,
												        0.0_025,
												    ]						)

												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       lowerCamelCase			(   self     :List[Any]						):
												A						=    torch.device("cpu"						)






												class    _UpperCAmelCase  :
																	UpperCamelCase						=				1







												A						=    self.get_dummy_components()

												A						=    self.pipeline_class(**__UpperCamelCase						)
												A						=    pipe.to(__UpperCamelCase						)

												A						=    components["prior"]
												A						=    components["decoder"]
												A						=    components["super_res_first"]
												A						=    components["tokenizer"]
												A						=    components["text_encoder"]

												A						=    torch.Generator(device=__UpperCamelCase						).manual_seed(0						)
												A						=    prior.dtype
												A						=    1

												A						=    (batch_size, prior.config.embedding_dim)
												A						=    pipe.prepare_latents(
												    __UpperCamelCase	,			dtype=__UpperCamelCase	,			device=__UpperCamelCase	,			generator=__UpperCamelCase	,			latents=__UpperCamelCase	,			scheduler=DummyScheduler()						)
												A						=    (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
												A						=    pipe.prepare_latents(
												    __UpperCamelCase	,			dtype=__UpperCamelCase	,			device=__UpperCamelCase	,			generator=__UpperCamelCase	,			latents=__UpperCamelCase	,			scheduler=DummyScheduler()						)

												A						=    (
												    batch_size,
												    super_res_first.config.in_channels // 2,
												    super_res_first.config.sample_size,
												    super_res_first.config.sample_size,
												)
												A						=    pipe.prepare_latents(
												    __UpperCamelCase	,			dtype=__UpperCamelCase	,			device=__UpperCamelCase	,			generator=__UpperCamelCase	,			latents=__UpperCamelCase	,			scheduler=DummyScheduler()						)

												pipe.set_progress_bar_config(disable=__UpperCamelCase						)

												A						=    "this is a prompt example"

												A						=    torch.Generator(device=__UpperCamelCase						).manual_seed(0						)
												A						=    pipe(
												    [prompt]	,			generator=__UpperCamelCase	,			prior_num_inference_steps=2	,			decoder_num_inference_steps=2	,			super_res_num_inference_steps=2	,			prior_latents=__UpperCamelCase	,			decoder_latents=__UpperCamelCase	,			super_res_latents=__UpperCamelCase	,			output_type="np"	,			)
												A						=    output.images

												A						=    tokenizer(
												    __UpperCamelCase	,			padding="max_length"	,			max_length=tokenizer.model_max_length	,			return_tensors="pt"	,			)
												A						=    text_encoder(text_inputs.input_ids						)
												A						=    text_inputs.attention_mask

												A						=    torch.Generator(device=__UpperCamelCase						).manual_seed(0						)
												A						=    pipe(
												    generator=__UpperCamelCase	,			prior_num_inference_steps=2	,			decoder_num_inference_steps=2	,			super_res_num_inference_steps=2	,			prior_latents=__UpperCamelCase	,			decoder_latents=__UpperCamelCase	,			super_res_latents=__UpperCamelCase	,			text_model_output=__UpperCamelCase	,			text_attention_mask=__UpperCamelCase	,			output_type="np"	,			)[0]

												# make sure passing text embeddings manually is identical
												assert np.abs(image - image_from_text						).max() < 1e-4


					@skip_mps
					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    torch_device == "cpu"

												self._test_attention_slicing_forward_pass(test_max_difference=__UpperCamelCase	,			expected_max_diff=0.01						)


					@skip_mps
					def       lowerCamelCase			(   self     :Optional[Any]						):
												A						=    torch_device == "cpu"
												A						=    True
												A						=    [
												    "prior_num_inference_steps",
												    "decoder_num_inference_steps",
												    "super_res_num_inference_steps",
												]

												self._test_inference_batch_single_identical(
												    test_max_difference=__UpperCamelCase	,			relax_max_difference=__UpperCamelCase	,			additional_params_copy_to_batched_inputs=__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :Dict						):
												A						=    [
												    "prior_num_inference_steps",
												    "decoder_num_inference_steps",
												    "super_res_num_inference_steps",
												]

												if torch_device == "mps":
																			# TODO: MPS errors with larger batch sizes
																			A						=    [2, 3]
																			self._test_inference_batch_consistent(
																			    batch_sizes=__UpperCamelCase	,			additional_params_copy_to_batched_inputs=__UpperCamelCase	,			)
												else:
																			self._test_inference_batch_consistent(
																			    additional_params_copy_to_batched_inputs=__UpperCamelCase						)


					@skip_mps
					def       lowerCamelCase			(   self     :Optional[int]						):
												return super().test_dict_tuple_outputs_equivalent()


					@skip_mps
					def       lowerCamelCase			(   self     :Any						):
												return super().test_save_load_local()


					@skip_mps
					def       lowerCamelCase			(   self     :str						):
												return super().test_save_load_optional_components()







@nightly
class    _UpperCAmelCase  (      unittest.TestCase					):


					def       lowerCamelCase			(   self     :Optional[int]						):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/unclip/karlo_v1_alpha_horse_cpu.npy"						)

												A						=    UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"						)
												pipeline.set_progress_bar_config(disable=__UpperCamelCase						)

												A						=    torch.manual_seed(0						)
												A						=    pipeline(
												    "horse"	,			num_images_per_prompt=1	,			generator=__UpperCamelCase	,			output_type="np"	,			)

												A						=    output.images[0]

												assert image.shape == (2_56, 2_56, 3)
												assert np.abs(expected_image - image						).max() < 1e-1







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


					def       lowerCamelCase			(   self     :Dict						):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    load_numpy(
												    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
												    "/unclip/karlo_v1_alpha_horse_fp16.npy"						)

												A						=    UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"	,			torch_dtype=torch.floataa						)
												A						=    pipeline.to(__UpperCamelCase						)
												pipeline.set_progress_bar_config(disable=__UpperCamelCase						)

												A						=    torch.Generator(device="cpu"						).manual_seed(0						)
												A						=    pipeline(
												    "horse"	,			generator=__UpperCamelCase	,			output_type="np"	,			)

												A						=    output.images[0]

												assert image.shape == (2_56, 2_56, 3)

												assert_mean_pixel_difference(__UpperCamelCase	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												A						=    UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"	,			torch_dtype=torch.floataa						)
												A						=    pipe.to(__UpperCamelCase						)
												pipe.set_progress_bar_config(disable=__UpperCamelCase						)
												pipe.enable_attention_slicing()
												pipe.enable_sequential_cpu_offload()

												A						=    pipe(
												    "horse"	,			num_images_per_prompt=1	,			prior_num_inference_steps=2	,			decoder_num_inference_steps=2	,			super_res_num_inference_steps=2	,			output_type="np"	,			)

												A						=    torch.cuda.max_memory_allocated()
												# make sure that less than 7 GB is allocated
												assert mem_bytes < 7 * 10**9









"""simple docstring"""
import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_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 ResNetBackbone, ResNetForImageClassification, ResNetModel
		from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class    _UpperCAmelCase  :


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :Optional[int]=32	,			__UpperCamelCase     :str=3	,			__UpperCamelCase     :Union[str, Any]=10	,			__UpperCamelCase     :Union[str, Any]=[10, 20, 30, 40]	,			__UpperCamelCase     :int=[1, 1, 2, 1]	,			__UpperCamelCase     :Dict=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :Any="relu"	,			__UpperCamelCase     :Optional[Any]=3	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :str=["stage2", "stage3", "stage4"]	,			__UpperCamelCase     :str=[2, 3, 4]	,			):
												A						=    parent
												A						=    batch_size
												A						=    image_size
												A						=    num_channels
												A						=    embeddings_size
												A						=    hidden_sizes
												A						=    depths
												A						=    is_training
												A						=    use_labels
												A						=    hidden_act
												A						=    num_labels
												A						=    scope
												A						=    len(__UpperCamelCase						)
												A						=    out_features
												A						=    out_indices


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

												A						=    self.get_config()

												return config, pixel_values, labels


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return ResNetConfig(
												    num_channels=self.num_channels	,			embeddings_size=self.embeddings_size	,			hidden_sizes=self.hidden_sizes	,			depths=self.depths	,			hidden_act=self.hidden_act	,			num_labels=self.num_labels	,			out_features=self.out_features	,			out_indices=self.out_indices	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :List[Any]						):
												A						=    ResNetModel(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)
												# expected last hidden states: B, C, H // 32, W // 32
												self.parent.assertEqual(
												    result.last_hidden_state.shape	,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)	,			)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :str						):
												A						=    self.num_labels
												A						=    ResNetForImageClassification(__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase	,			labels=__UpperCamelCase						)
												self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Optional[Any]	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    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						)	,			[self.batch_size, self.hidden_sizes[1], 4, 4]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			len(config.out_features						)						)
												self.parent.assertListEqual(model.channels	,			config.hidden_sizes[1:]						)

												# verify backbone works with out_features=None
												A						=    None
												A						=    ResNetBackbone(config=__UpperCamelCase						)
												model.to(__UpperCamelCase						)
												model.eval()
												A						=    model(__UpperCamelCase						)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps						)	,			1						)
												self.parent.assertListEqual(list(result.feature_maps[0].shape						)	,			[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

												# verify channels
												self.parent.assertEqual(len(model.channels						)	,			1						)
												self.parent.assertListEqual(model.channels	,			[config.hidden_sizes[-1]]						)


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    self.prepare_config_and_inputs()
												A,      A,      A						=    config_and_inputs
												A						=    {"pixel_values": pixel_values}
												return config, inputs_dict







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					UpperCamelCase						=				(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCamelCase						=				True
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False
					UpperCamelCase						=				False


					def       lowerCamelCase			(   self     :List[Any]						):
												A						=    ResNetModelTester(self						)
												A						=    ConfigTester(self	,			config_class=__UpperCamelCase	,			has_text_modality=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]						):
												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       lowerCamelCase			(   self     :Union[str, Any]						):
												return


					@unittest.skip(reason="ResNet does not use inputs_embeds"						)
					def       lowerCamelCase			(   self     :Optional[int]						):
												pass


					@unittest.skip(reason="ResNet does not support input and output embeddings"						)
					def       lowerCamelCase			(   self     :Optional[Any]						):
												pass


					def       lowerCamelCase			(   self     :List[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

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

																			A						=    ["pixel_values"]
																			self.assertListEqual(arg_names[:1]	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Tuple						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Union[str, Any]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			A						=    model_class(config=__UpperCamelCase						)
																			for name, module in model.named_modules():
																										if isinstance(__UpperCamelCase	,			(nn.BatchNormad, nn.GroupNorm)						):
																																	self.assertTrue(
																																	    torch.all(module.weight == 1						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)
																																	self.assertTrue(
																																	    torch.all(module.bias == 0						)	,			msg=f"Parameter {name} of model {model_class} seems not properly initialized"	,			)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												def check_hidden_states_output(__UpperCamelCase     :int	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :Union[str, Any]						):
																			A						=    model_class(__UpperCamelCase						)
																			model.to(__UpperCamelCase						)
																			model.eval()

																			with torch.no_grad():
																										A						=    model(**self._prepare_for_class(__UpperCamelCase	,			__UpperCamelCase						)						)

																			A						=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			A						=    self.model_tester.num_stages
																			self.assertEqual(len(__UpperCamelCase						)	,			expected_num_stages + 1						)

																			# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]						)	,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]	,			)

												A,      A						=    self.model_tester.prepare_config_and_inputs_for_common()
												A						=    ["basic", "bottleneck"]
												for model_class in self.all_model_classes:
																			for layer_type in layers_type:
																										A						=    layer_type
																										A						=    True
																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)

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

																										check_hidden_states_output(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)


					@unittest.skip(reason="ResNet does not use feedforward chunking"						)
					def       lowerCamelCase			(   self     :Tuple						):
												pass


					def       lowerCamelCase			(   self     :Optional[int]						):
												A						=    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


					@slow
					def       lowerCamelCase			(   self     :Union[str, Any]						):
												for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A						=    ResNetModel.from_pretrained(__UpperCamelCase						)
																			self.assertIsNotNone(__UpperCamelCase						)







def    A__							(     ):
							A						=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image






@require_torch
@require_vision
class    _UpperCAmelCase  (      unittest.TestCase					):


					@cached_property
					def       lowerCamelCase			(   self     :List[Any]						):
												return (
												    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
												    if is_vision_available()
												    else None
												)


					@slow
					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

												A						=    self.default_image_processor
												A						=    prepare_img()
												A						=    image_processor(images=__UpperCamelCase	,			return_tensors="pt"						).to(__UpperCamelCase						)

												# forward pass
												with torch.no_grad():
																			A						=    model(**__UpperCamelCase						)

												# verify the logits
												A						=    torch.Size((1, 10_00)						)
												self.assertEqual(outputs.logits.shape	,			__UpperCamelCase						)

												A						=    torch.tensor([-11.1_069, -9.7_877, -8.3_777]						).to(__UpperCamelCase						)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,			__UpperCamelCase	,			atol=1e-4						)						)







@require_torch
class    _UpperCAmelCase  (      lowercase_	,				unittest.TestCase					):
					UpperCamelCase						=				(ResNetBackbone,) if is_torch_available() else ()
					UpperCamelCase						=				False
					UpperCamelCase						=				ResNetConfig


					def       lowerCamelCase			(   self     :Dict						):
												A						=    ResNetModelTester(self						)


