


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__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[str, Any]      =  ShapEImgaImgPipeline
    UpperCAmelCase__						:		int      =  ["image"]
    UpperCAmelCase__						:		List[str]      =  ["image"]
    UpperCAmelCase__						:		int      =  [
        "num_images_per_prompt",
        "num_inference_steps",
        "generator",
        "latents",
        "guidance_scale",
        "frame_size",
        "output_type",
        "return_dict",
    ]
    UpperCAmelCase__						:		List[Any]      =  False






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






    @property
    def _a					(			self    )  ->       int:
         return 32






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






    @property
    def _a					(			self    )  ->       Any:
         return 8






    @property
    def _a					(			self    )  ->       int:
         torch.manual_seed(0    )
         __UpperCamelCase    							=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					,   )

         __UpperCamelCase    							=CLIPVisionModel(A_    )
         return model






    @property
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=CLIPImageProcessor(
             crop_size=224					,   do_center_crop=A_					,   do_normalize=A_					,   do_resize=A_					,   image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]					,   image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]					,   resample=3					,   size=224					,   )

         return image_processor






    @property
    def _a					(			self    )  ->       str:
         torch.manual_seed(0    )

         __UpperCamelCase    							={
             '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,
         }

         __UpperCamelCase    							=PriorTransformer(**A_    )
         return model






    @property
    def _a					(			self    )  ->       int:
         torch.manual_seed(0    )

         __UpperCamelCase    							={
             '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,
             ),
         }
         __UpperCamelCase    							=ShapERenderer(**A_    )
         return model






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.dummy_prior
         __UpperCamelCase    							=self.dummy_image_encoder
         __UpperCamelCase    							=self.dummy_image_processor
         __UpperCamelCase    							=self.dummy_renderer

         __UpperCamelCase    							=HeunDiscreteScheduler(
             beta_schedule='exp'					,   num_train_timesteps=1024					,   prediction_type='sample'					,   use_karras_sigmas=A_					,   clip_sample=A_					,   clip_sample_range=1.0					,   )
         __UpperCamelCase    							={
             'prior': prior,
             'image_encoder': image_encoder,
             'image_processor': image_processor,
             'renderer': renderer,
             'scheduler': scheduler,
         }

         return components






    def _a					(			self					,   A_					,   A_=0    )  ->       Any:
         __UpperCamelCase    							=floats_tensor((1, 3, 64, 64)					,   rng=random.Random(A_    )    ).to(A_    )

         if str(A_    ).startswith('mps'    ):
              __UpperCamelCase    							=torch.manual_seed(A_    )
         else:
              __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(A_    )
         __UpperCamelCase    							={
             'image': input_image,
             'generator': generator,
             'num_inference_steps': 1,
             'frame_size': 32,
             'output_type': 'np',
         }
         return inputs






    def _a					(			self    )  ->       str:
         __UpperCamelCase    							='cpu'

         __UpperCamelCase    							=self.get_dummy_components()

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

         pipe.set_progress_bar_config(disable=A_    )

         __UpperCamelCase    							=pipe(**self.get_dummy_inputs(A_    )    )
         __UpperCamelCase    							=output.images[0]
         __UpperCamelCase    							=image[0, -3:, -3:, -1]

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

         __UpperCamelCase    							=np.array(
             [
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
                 0.0003_9216,
             ]    )

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






    def _a					(			self    )  ->       int:
         # 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    )  ->       List[str]:
         __UpperCamelCase    							=torch_device == 'cpu'
         __UpperCamelCase    							=True
         self._test_inference_batch_single_identical(
             batch_size=2					,   test_max_difference=A_					,   relax_max_difference=A_					,   )






    def _a					(			self    )  ->       Tuple:
         __UpperCamelCase    							=self.get_dummy_components()
         __UpperCamelCase    							=self.pipeline_class(**A_    )
         __UpperCamelCase    							=pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )

         __UpperCamelCase    							=1
         __UpperCamelCase    							=2

         __UpperCamelCase    							=self.get_dummy_inputs(A_    )

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

         __UpperCamelCase    							=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		):



    """simple docstring"""






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






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'    )
         __UpperCamelCase    							=load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/shap_e/test_shap_e_img2img_out.npy'    )
         __UpperCamelCase    							=ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'    )
         __UpperCamelCase    							=pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )

         __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(0    )

         __UpperCamelCase    							=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_    )









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "yolos"






    def __init__(			self					,   A_=768					,   A_=12					,   A_=12					,   A_=3072					,   A_="gelu"					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=1E-12					,   A_=[512, 864]					,   A_=16					,   A_=3					,   A_=True					,   A_=100					,   A_=True					,   A_=False					,   A_=1					,   A_=5					,   A_=2					,   A_=5					,   A_=2					,   A_=0.1					,   **A_					,   )  ->       Any:
         super().__init__(**A_    )

         __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    							=initializer_range
         __UpperCamelCase    							=layer_norm_eps
         __UpperCamelCase    							=image_size
         __UpperCamelCase    							=patch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=qkv_bias
         __UpperCamelCase    							=num_detection_tokens
         __UpperCamelCase    							=use_mid_position_embeddings
         __UpperCamelCase    							=auxiliary_loss
         # Hungarian matcher
         __UpperCamelCase    							=class_cost
         __UpperCamelCase    							=bbox_cost
         __UpperCamelCase    							=giou_cost
         # Loss coefficients
         __UpperCamelCase    							=bbox_loss_coefficient
         __UpperCamelCase    							=giou_loss_coefficient
         __UpperCamelCase    							=eos_coefficient






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str      =  version.parse("1.11"		)






    @property
    def _a					(			self    )  ->       Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]    )






    @property
    def _a					(			self    )  ->       float:
         return 1E-4






    @property
    def _a					(			self    )  ->       int:
         return 12





