

import inspect
import unittest

from transformers import ViTMSNConfig
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_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 ViTMSNForImageClassification, ViTMSNModel
     from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor





class  __UpperCAmelCase					:



   def __init__(							self							:					str,		__A							:					List[str],		__A							:					int=1_3,		__A							:					Optional[int]=3_0,		__A							:					Any=2,		__A							:					Any=3,		__A							:					str=True,		__A							:					int=True,		__A							:					Optional[Any]=3_2,		__A							:					str=5,		__A							:					Union[str, Any]=4,		__A							:					Optional[int]=3_7,		__A							:					Dict="gelu",		__A							:					Tuple=0.1,		__A							:					List[str]=0.1,		__A							:					Union[str, Any]=1_0,		__A							:					Optional[Any]=0.0_2,		__A							:					Any=None,		):
     UpperCAmelCase	:							Optional[Any]						=			parent
     UpperCAmelCase	:							Dict						=			batch_size
     UpperCAmelCase	:							Union[str, Any]						=			image_size
     UpperCAmelCase	:							List[str]						=			patch_size
     UpperCAmelCase	:							Any						=			num_channels
     UpperCAmelCase	:							Tuple						=			is_training
     UpperCAmelCase	:							Dict						=			use_labels
     UpperCAmelCase	:							Union[str, Any]						=			hidden_size
     UpperCAmelCase	:							Union[str, Any]						=			num_hidden_layers
     UpperCAmelCase	:							Any						=			num_attention_heads
     UpperCAmelCase	:							Optional[Any]						=			intermediate_size
     UpperCAmelCase	:							Optional[int]						=			hidden_act
     UpperCAmelCase	:							str						=			hidden_dropout_prob
     UpperCAmelCase	:							List[Any]						=			attention_probs_dropout_prob
     UpperCAmelCase	:							Dict						=			type_sequence_label_size
     UpperCAmelCase	:							List[Any]						=			initializer_range
     UpperCAmelCase	:							int						=			scope

     # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
     UpperCAmelCase	:							Dict						=			(image_size // patch_size) ** 2
     UpperCAmelCase	:							Tuple						=			num_patches + 1



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Union[str, Any]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     UpperCAmelCase	:							Any						=			None
     if self.use_labels:
       UpperCAmelCase	:							Union[str, Any]						=			ids_tensor([self.batch_size],		self.type_sequence_label_size						)

     UpperCAmelCase	:							List[Any]						=			self.get_config()

     return config, pixel_values, labels



   def 	__magic_name__   (							self							:					Optional[Any]						):
     return ViTMSNConfig(
         image_size=self.image_size,		patch_size=self.patch_size,		num_channels=self.num_channels,		hidden_size=self.hidden_size,		num_hidden_layers=self.num_hidden_layers,		num_attention_heads=self.num_attention_heads,		intermediate_size=self.intermediate_size,		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 	__magic_name__   (							self							:					List[str],		__A							:					List[str],		__A							:					Union[str, Any],		__A							:					str						):
     UpperCAmelCase	:							List[Any]						=			ViTMSNModel(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Any						=			model(__A						)
     self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)						)



   def 	__magic_name__   (							self							:					List[str],		__A							:					Any,		__A							:					Any,		__A							:					List[str]						):
     UpperCAmelCase	:							Union[str, Any]						=			self.type_sequence_label_size
     UpperCAmelCase	:							List[str]						=			ViTMSNForImageClassification(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Optional[Any]						=			model(__A,		labels=__A						)
     print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''						)
     print('''Labels: {labels}'''						)
     self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)						)

     # test greyscale images
     UpperCAmelCase	:							Union[str, Any]						=			1
     UpperCAmelCase	:							List[str]						=			ViTMSNForImageClassification(__A						)
     model.to(__A						)
     model.eval()

     UpperCAmelCase	:							Any						=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
     UpperCAmelCase	:							Tuple						=			model(__A						)
     self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.type_sequence_label_size)						)





   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Dict						=			self.prepare_config_and_inputs()
     UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[str]						=			config_and_inputs
     UpperCAmelCase	:							Any						=			{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
   UpperCamelCase       					=					(
       {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
       if is_torch_available()
       else {}
   )

   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Optional[int]						=			ViTMSNModelTester(self						)
     UpperCAmelCase	:							Union[str, Any]						=			ConfigTester(self,		config_class=__A,		has_text_modality=__A,		hidden_size=3_7						)



   def 	__magic_name__   (							self							:					Optional[int]						):
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''						)
   def 	__magic_name__   (							self							:					int						):
     pass



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase			,						UpperCAmelCase	:							Optional[int]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:							List[Any]						=			['''pixel_values''']
       self.assertListEqual(arg_names[:1],		__A						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[Any]						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__A						)





   @slow
   def 	__magic_name__   (							self							:					Optional[int]						):
     for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:							List[str]						=			ViTMSNModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)






def       a__					(		)  ->						Any:
  UpperCAmelCase	:							Tuple						=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
  return image






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



   @cached_property
   def 	__magic_name__   (							self							:					str						):
     return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''						) if is_vision_available() else None





   @slow
   def 	__magic_name__   (							self							:					List[str]						):
     torch.manual_seed(2						)
     UpperCAmelCase	:							Optional[int]						=			ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''						).to(__A						)

     UpperCAmelCase	:							List[Any]						=			self.default_image_processor
     UpperCAmelCase	:							List[str]						=			prepare_img()
     UpperCAmelCase	:							Dict						=			image_processor(images=__A,		return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase	:							Any						=			model(**__A						)

     # verify the logits
     UpperCAmelCase	:							Union[str, Any]						=			torch.Size((1, 1_0_0_0)						)
     self.assertEqual(outputs.logits.shape,		__A						)

     UpperCAmelCase	:							int						=			torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]						).to(__A						)

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








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					ShapEImgaImgPipeline
   UpperCamelCase       					=					["""image"""]
   UpperCamelCase       					=					["""image"""]
   UpperCamelCase       					=					[
       """num_images_per_prompt""",
       """num_inference_steps""",
       """generator""",
       """latents""",
       """guidance_scale""",
       """frame_size""",
       """output_type""",
       """return_dict""",
   ]
   UpperCamelCase       					=					False



   @property
   def 	__magic_name__   (							self							:					Optional[Any]						):
     return 3_2



   @property
   def 	__magic_name__   (							self							:					Optional[int]						):
     return 3_2



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return self.time_input_dim * 4



   @property
   def 	__magic_name__   (							self							:					Any						):
     return 8



   @property
   def 	__magic_name__   (							self							:					List[str]						):
     torch.manual_seed(0						)
     UpperCAmelCase	:							str						=			CLIPVisionConfig(
         hidden_size=self.text_embedder_hidden_size,		image_size=6_4,		projection_dim=self.text_embedder_hidden_size,		intermediate_size=3_7,		num_attention_heads=4,		num_channels=3,		num_hidden_layers=5,		patch_size=1,		)

     UpperCAmelCase	:							Any						=			CLIPVisionModel(__A						)
     return model



   @property
   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			CLIPImageProcessor(
         crop_size=2_2_4,		do_center_crop=__A,		do_normalize=__A,		do_resize=__A,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],		resample=3,		size=2_2_4,		)

     return image_processor



   @property
   def 	__magic_name__   (							self							:					Dict						):
     torch.manual_seed(0						)

     UpperCAmelCase	:							Any						=			{
         '''num_attention_heads''': 2,
         '''attention_head_dim''': 1_6,
         '''embedding_dim''': self.time_input_dim,
         '''num_embeddings''': 3_2,
         '''embedding_proj_dim''': self.text_embedder_hidden_size,
         '''time_embed_dim''': self.time_embed_dim,
         '''num_layers''': 1,
         '''clip_embed_dim''': self.time_input_dim * 2,
         '''additional_embeddings''': 0,
         '''time_embed_act_fn''': '''gelu''',
         '''norm_in_type''': '''layer''',
         '''embedding_proj_norm_type''': '''layer''',
         '''encoder_hid_proj_type''': None,
         '''added_emb_type''': None,
     }

     UpperCAmelCase	:							List[Any]						=			PriorTransformer(**__A						)
     return model



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     torch.manual_seed(0						)

     UpperCAmelCase	:							List[Any]						=			{
         '''param_shapes''': (
             (self.renderer_dim, 9_3),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
             (self.renderer_dim, 8),
         ),
         '''d_latent''': self.time_input_dim,
         '''d_hidden''': self.renderer_dim,
         '''n_output''': 1_2,
         '''background''': (
             0.1,
             0.1,
             0.1,
         ),
     }
     UpperCAmelCase	:							List[str]						=			ShapERenderer(**__A						)
     return model



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							str						=			self.dummy_prior
     UpperCAmelCase	:							List[str]						=			self.dummy_image_encoder
     UpperCAmelCase	:							List[Any]						=			self.dummy_image_processor
     UpperCAmelCase	:							Dict						=			self.dummy_renderer

     UpperCAmelCase	:							int						=			HeunDiscreteScheduler(
         beta_schedule='''exp''',		num_train_timesteps=1_0_2_4,		prediction_type='''sample''',		use_karras_sigmas=__A,		clip_sample=__A,		clip_sample_range=1.0,		)
     UpperCAmelCase	:							List[str]						=			{
         '''prior''': prior,
         '''image_encoder''': image_encoder,
         '''image_processor''': image_processor,
         '''renderer''': renderer,
         '''scheduler''': scheduler,
     }

     return components



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Dict,		__A							:					List[Any]=0						):
     UpperCAmelCase	:							int						=			floats_tensor((1, 3, 6_4, 6_4),		rng=random.Random(__A						)						).to(__A						)

     if str(__A						).startswith('''mps'''						):
       UpperCAmelCase	:							List[str]						=			torch.manual_seed(__A						)
     else:
       UpperCAmelCase	:							Optional[Any]						=			torch.Generator(device=__A						).manual_seed(__A						)
     UpperCAmelCase	:							Dict						=			{
         '''image''': input_image,
         '''generator''': generator,
         '''num_inference_steps''': 1,
         '''frame_size''': 3_2,
         '''output_type''': '''np''',
     }
     return inputs



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Dict						=			'''cpu'''

     UpperCAmelCase	:							Any						=			self.get_dummy_components()

     UpperCAmelCase	:							Tuple						=			self.pipeline_class(**__A						)
     UpperCAmelCase	:							Dict						=			pipe.to(__A						)

     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							List[Any]						=			pipe(**self.get_dummy_inputs(__A						)						)
     UpperCAmelCase	:							Any						=			output.images[0]
     UpperCAmelCase	:							List[str]						=			image[0, -3:, -3:, -1]

     assert image.shape == (2_0, 3_2, 3_2, 3)

     UpperCAmelCase	:							Optional[int]						=			np.array(
         [
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
             0.0_0_0_3_9_2_1_6,
         ]						)

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



   def 	__magic_name__   (							self							:					Dict						):
     # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
     self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							Tuple						=			torch_device == '''cpu'''
     UpperCAmelCase	:							List[Any]						=			True
     self._test_inference_batch_single_identical(
         batch_size=2,		test_max_difference=__A,		relax_max_difference=__A,		)





   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Dict						=			self.get_dummy_components()
     UpperCAmelCase	:							Union[str, Any]						=			self.pipeline_class(**__A						)
     UpperCAmelCase	:							List[str]						=			pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Any						=			1
     UpperCAmelCase	:							Any						=			2

     UpperCAmelCase	:							Optional[int]						=			self.get_dummy_inputs(__A						)

     for key in inputs.keys():
       if key in self.batch_params:
         UpperCAmelCase	:							Any						=			batch_size * [inputs[key]]

     UpperCAmelCase	:							int						=			pipe(**__A,		num_images_per_prompt=__A						)[0]

     assert images.shape[0] == batch_size * num_images_per_prompt






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



   def 	__magic_name__   (							self							:					List[Any]						):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()





   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							List[str]						=			load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''						)
     UpperCAmelCase	:							Dict						=			load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/shap_e/test_shap_e_img2img_out.npy'''						)
     UpperCAmelCase	:							Union[str, Any]						=			ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''						)
     UpperCAmelCase	:							int						=			pipe.to(__A						)
     pipe.set_progress_bar_config(disable=__A						)

     UpperCAmelCase	:							Dict						=			torch.Generator(device=__A						).manual_seed(0						)

     UpperCAmelCase	:							List[str]						=			pipe(
         __A,		generator=__A,		guidance_scale=3.0,		num_inference_steps=6_4,		frame_size=6_4,		output_type='''np''',		).images[0]

     assert images.shape == (2_0, 6_4, 6_4, 3)

     assert_mean_pixel_difference(__A,		__A						)





