

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  __A(						_snake_case						,			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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 32

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 32

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.time_input_dim * 4

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 8

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            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(lowerCAmelCase__      )
            return model

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						CLIPImageProcessor(
                crop_size=224,       do_center_crop=lowerCAmelCase__,       do_normalize=lowerCAmelCase__,       do_resize=lowerCAmelCase__,       image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073],       image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711],       resample=3,       size=224,       )

            return image_processor

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            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(**lowerCAmelCase__      )
            return model

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            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(**lowerCAmelCase__      )
            return model

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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=lowerCAmelCase__,       clip_sample=lowerCAmelCase__,       clip_sample_range=1.0,       )
            _UpperCamelCase              =						{
                '''prior''': prior,
                '''image_encoder''': image_encoder,
                '''image_processor''': image_processor,
                '''renderer''': renderer,
                '''scheduler''': scheduler,
            }

            return components

     def 	_UpperCamelCase     (      self,       A,       A=0      ):





            """simple docstring"""

            _UpperCamelCase              =						floats_tensor((1, 3, 64, 64),       rng=random.Random(lowerCAmelCase__      )      ).to(lowerCAmelCase__      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''cpu'''

            _UpperCamelCase              =						self.get_dummy_components()

            _UpperCamelCase              =						self.pipeline_class(**lowerCAmelCase__      )
            _UpperCamelCase              =						pipe.to(lowerCAmelCase__      )

            pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

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

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

            _UpperCamelCase              =						np.array(
                [
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                    0.00_039_216,
                ]      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            self._test_inference_batch_consistent(batch_sizes=[1, 2]      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						torch_device == '''cpu'''
            _UpperCamelCase              =						True
            self._test_inference_batch_single_identical(
                batch_size=2,       test_max_difference=lowerCAmelCase__,       relax_max_difference=lowerCAmelCase__,       )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_dummy_components()
            _UpperCamelCase              =						self.pipeline_class(**lowerCAmelCase__      )
            _UpperCamelCase              =						pipe.to(lowerCAmelCase__      )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

            _UpperCamelCase              =						1
            _UpperCamelCase              =						2

            _UpperCamelCase              =						self.get_dummy_inputs(lowerCAmelCase__      )

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

            _UpperCamelCase              =						pipe(**lowerCAmelCase__,       num_images_per_prompt=lowerCAmelCase__      )[0]

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






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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _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(lowerCAmelCase__      )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__      )

            _UpperCamelCase              =						torch.Generator(device=lowerCAmelCase__      ).manual_seed(0      )

            _UpperCamelCase              =						pipe(
                lowerCAmelCase__,       generator=lowerCAmelCase__,       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(lowerCAmelCase__,       lowerCAmelCase__      )













import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowercase    : Optional[int]    	=  logging.get_logger(__name__)

lowercase    : List[Any]    	=  {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  __A(						__UpperCAmelCase ):
     __A	  =       "detr"
     __A	  =       ["past_key_values"]
     __A	  =       {
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }

     def __init__(      self,       A=True,       A=None,       A=3,       A=100,       A=6,       A=2048,       A=8,       A=6,       A=2048,       A=8,       A=0.0,       A=0.0,       A=True,       A="relu",       A=256,       A=0.1,       A=0.0,       A=0.0,       A=0.02,       A=1.0,       A=False,       A="sine",       A="resnet50",       A=True,       A=False,       A=1,       A=5,       A=2,       A=1,       A=1,       A=5,       A=2,       A=0.1,       **A,       ):





            """simple docstring"""

            if backbone_config is not None and use_timm_backbone:
                   raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''      )

            if not use_timm_backbone:
                   if backbone_config is None:
                          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']      )
                   elif isinstance(A,       A      ):
                          _UpperCamelCase              =						backbone_config.get('''model_type'''      )
                          _UpperCamelCase              =						CONFIG_MAPPING[backbone_model_type]
                          _UpperCamelCase              =						config_class.from_dict(A      )
                   # set timm attributes to None
                   _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						None, None, None

            _UpperCamelCase              =						use_timm_backbone
            _UpperCamelCase              =						backbone_config
            _UpperCamelCase              =						num_channels
            _UpperCamelCase              =						num_queries
            _UpperCamelCase              =						d_model
            _UpperCamelCase              =						encoder_ffn_dim
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						encoder_attention_heads
            _UpperCamelCase              =						decoder_ffn_dim
            _UpperCamelCase              =						decoder_layers
            _UpperCamelCase              =						decoder_attention_heads
            _UpperCamelCase              =						dropout
            _UpperCamelCase              =						attention_dropout
            _UpperCamelCase              =						activation_dropout
            _UpperCamelCase              =						activation_function
            _UpperCamelCase              =						init_std
            _UpperCamelCase              =						init_xavier_std
            _UpperCamelCase              =						encoder_layerdrop
            _UpperCamelCase              =						decoder_layerdrop
            _UpperCamelCase              =						encoder_layers
            _UpperCamelCase              =						auxiliary_loss
            _UpperCamelCase              =						position_embedding_type
            _UpperCamelCase              =						backbone
            _UpperCamelCase              =						use_pretrained_backbone
            _UpperCamelCase              =						dilation
            # Hungarian matcher
            _UpperCamelCase              =						class_cost
            _UpperCamelCase              =						bbox_cost
            _UpperCamelCase              =						giou_cost
            # Loss coefficients
            _UpperCamelCase              =						mask_loss_coefficient
            _UpperCamelCase              =						dice_loss_coefficient
            _UpperCamelCase              =						bbox_loss_coefficient
            _UpperCamelCase              =						giou_loss_coefficient
            _UpperCamelCase              =						eos_coefficient
            super().__init__(is_encoder_decoder=A,       **A      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.encoder_attention_heads

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return self.d_model

     @classmethod
     def 	_UpperCamelCase     (      cls,       A,       **A      ):





            """simple docstring"""

            return cls(backbone_config=A,       **A      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						copy.deepcopy(self.__dict__      )
            if output["backbone_config"] is not None:
                   _UpperCamelCase              =						self.backbone_config.to_dict()
            _UpperCamelCase              =						self.__class__.model_type
            return output






class  __A(						__UpperCAmelCase ):
     __A	  =       version.parse("1.11" )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                    ('''pixel_mask''', {0: '''batch'''}),
                ]      )

     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 1E-5



     @property
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            return 12






