




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 						lowercase							(   UpperCamelCase__,unittest.TestCase		):
     _a							 =						ShapEImgaImgPipeline
     _a							 =						["image"]
     _a							 =						["image"]
     _a							 =						[
         "num_images_per_prompt",
         "num_inference_steps",
         "generator",
         "latents",
         "guidance_scale",
         "frame_size",
         "output_type",
         "return_dict",
     ]
     _a							 =						False

     @property
     def 					a__	(  self				)						->							Optional[int]:
           return 32

     @property
     def 					a__	(  self				)						->							Tuple:
           return 32

     @property
     def 					a__	(  self				)						->							Optional[Any]:
           return self.time_input_dim * 4

     @property
     def 					a__	(  self				)						->							Optional[int]:
           return 8

     @property
     def 					a__	(  self				)						->							List[Any]:
           torch.manual_seed(0				)
           _A							:     Union[str, Any]			=						CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size		,     image_size=64		,     projection_dim=self.text_embedder_hidden_size		,     intermediate_size=37		,     num_attention_heads=4		,     num_channels=3		,     num_hidden_layers=5		,     patch_size=1		,     )

           _A							:     List[Any]			=						CLIPVisionModel(_a				)
           return model

     @property
     def 					a__	(  self				)						->							Dict:
           _A							:     int			=						CLIPImageProcessor(
               crop_size=224		,     do_center_crop=_a		,     do_normalize=_a		,     do_resize=_a		,     image_mean=[0.48145466, 0.4578275, 0.40821073]		,     image_std=[0.26862954, 0.26130258, 0.27577711]		,     resample=3		,     size=224		,     )

           return image_processor

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

           _A							:     str			=						{
               """num_attention_heads""": 2,
               """attention_head_dim""": 16,
               """embedding_dim""": self.time_input_dim,
               """num_embeddings""": 32,
               """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,
           }

           _A							:     Optional[int]			=						PriorTransformer(**_a				)
           return model

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

           _A							:     List[str]			=						{
               """param_shapes""": (
                   (self.renderer_dim, 93),
                   (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""": 12,
               """background""": (
                   0.1,
                   0.1,
                   0.1,
               ),
           }
           _A							:     int			=						ShapERenderer(**_a				)
           return model

     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						self.dummy_prior
           _A							:     Tuple			=						self.dummy_image_encoder
           _A							:     str			=						self.dummy_image_processor
           _A							:     Any			=						self.dummy_renderer

           _A							:     str			=						HeunDiscreteScheduler(
               beta_schedule="""exp"""		,     num_train_timesteps=1024		,     prediction_type="""sample"""		,     use_karras_sigmas=_a		,     clip_sample=_a		,     clip_sample_range=1.0		,     )
           _A							:     Union[str, Any]			=						{
               """prior""": prior,
               """image_encoder""": image_encoder,
               """image_processor""": image_processor,
               """renderer""": renderer,
               """scheduler""": scheduler,
           }

           return components

     def 					a__	(  self		,     _a		,     _a=0				)						->							int:
           _A							:     str			=						floats_tensor((1, 3, 64, 64)		,     rng=random.Random(_a				)				).to(_a				)

           if str(_a				).startswith("""mps"""				):
                 _A							:     List[Any]			=						torch.manual_seed(_a				)
           else:
                 _A							:     Tuple			=						torch.Generator(device=_a				).manual_seed(_a				)
           _A							:     List[Any]			=						{
               """image""": input_image,
               """generator""": generator,
               """num_inference_steps""": 1,
               """frame_size""": 32,
               """output_type""": """np""",
           }
           return inputs

     def 					a__	(  self				)						->							Tuple:
           _A							:     List[Any]			=						"""cpu"""

           _A							:     List[str]			=						self.get_dummy_components()

           _A							:     str			=						self.pipeline_class(**_a				)
           _A							:     Optional[int]			=						pipe.to(_a				)

           pipe.set_progress_bar_config(disable=_a				)

           _A							:     Tuple			=						pipe(**self.get_dummy_inputs(_a				)				)
           _A							:     Tuple			=						output.images[0]
           _A							:     Dict			=						image[0, -3:, -3:, -1]

           assert image.shape == (20, 32, 32, 3)

           _A							:     Dict			=						np.array(
               [
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
                   0.00039216,
               ]				)

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

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

     def 					a__	(  self				)						->							Tuple:
           _A							:     List[str]			=						torch_device == """cpu"""
           _A							:     Union[str, Any]			=						True
           self._test_inference_batch_single_identical(
               batch_size=2		,     test_max_difference=_a		,     relax_max_difference=_a		,     )



     def 					a__	(  self				)						->							Tuple:
           _A							:     int			=						self.get_dummy_components()
           _A							:     Optional[int]			=						self.pipeline_class(**_a				)
           _A							:     int			=						pipe.to(_a				)
           pipe.set_progress_bar_config(disable=_a				)

           _A							:     int			=						1
           _A							:     List[str]			=						2

           _A							:     str			=						self.get_dummy_inputs(_a				)

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

           _A							:     Union[str, Any]			=						pipe(**_a		,     num_images_per_prompt=_a				)[0]

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







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

     def 					a__	(  self				)						->							int:
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



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

           _A							:     Tuple			=						torch.Generator(device=_a				).manual_seed(0				)

           _A							:     Optional[Any]			=						pipe(
               _a		,     generator=_a		,     guidance_scale=3.0		,     num_inference_steps=64		,     frame_size=64		,     output_type="""np"""		,     ).images[0]

           assert images.shape == (20, 64, 64, 3)

           assert_mean_pixel_difference(_a		,     _a				)


'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    int=30   ,			__a    :    Union[str, Any]=2   ,			__a    :    Dict=3   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=32   ,			__a    :    Any=5   ,			__a    :    str=4   ,			__a    :    Optional[int]=37   ,			__a    :    Optional[int]="gelu"   ,			__a    :    List[str]=0.1   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=10   ,			__a    :    Optional[int]=0.02   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         UpperCAmelCase_           =   hidden_size
         UpperCAmelCase_           =   num_hidden_layers
         UpperCAmelCase_           =   num_attention_heads
         UpperCAmelCase_           =   intermediate_size
         UpperCAmelCase_           =   hidden_act
         UpperCAmelCase_           =   hidden_dropout_prob
         UpperCAmelCase_           =   attention_probs_dropout_prob
         UpperCAmelCase_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range

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



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   ViTConfig(
             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   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			)

         return config, pixel_values



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   FlaxViTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (self.image_size, self.image_size)
         UpperCAmelCase_           =   (self.patch_size, self.patch_size)
         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, num_patches + 1, self.hidden_size) )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   FlaxViTForImageClassification(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   FlaxViTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   FlaxViTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Tuple ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
                   UpperCAmelCase_           =   model_class(__a )

                   @jax.jit
                   def model_jitted(__a    :    Tuple   ,			**__a    :    List[Any] ):
                        return model(pixel_values=__a   ,			**__a )

                   with self.subTest("JIT Enabled" ):
                        UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   with self.subTest("JIT Disabled" ):
                        with jax.disable_jit():
                             UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   self.assertEqual(len(__a )   ,			len(__a ) )
                   for jitted_output, output in zip(__a   ,			__a ):
                        self.assertEqual(jitted_output.shape   ,			output.shape )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_           =   model_class_name.from_pretrained("google/vit-base-patch16-224" )
              UpperCAmelCase_           =   model(np.ones((1, 3, 224, 224) ) )
              self.assertIsNotNone(__a )




