

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   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Any      	=					ShapEImgaImgPipeline
 __SCREAMING_SNAKE_CASE					:Dict      	=					["""image"""]
 __SCREAMING_SNAKE_CASE					:Tuple      	=					["""image"""]
 __SCREAMING_SNAKE_CASE					:List[str]      	=					[
     """num_images_per_prompt""",
     """num_inference_steps""",
     """generator""",
     """latents""",
     """guidance_scale""",
     """frame_size""",
     """output_type""",
     """return_dict""",
 ]
 __SCREAMING_SNAKE_CASE					:Any      	=					False



 @property
 def 			snake_case__  (   self		:   Optional[int]      ):
      return 32



 @property
 def 			snake_case__  (   self		:   int      ):
      return 32



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



 @property
 def 			snake_case__  (   self		:   Tuple      ):
      return 8



 @property
 def 			snake_case__  (   self		:   List[str]      ):
      torch.manual_seed(0      )
      __magic_name__						       =    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			,				)

      __magic_name__						       =    CLIPVisionModel(a__      )
      return model



 @property
 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    CLIPImageProcessor(
          crop_size=224			,				do_center_crop=a__			,				do_normalize=a__			,				do_resize=a__			,				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 			snake_case__  (   self		:   List[Any]      ):
      torch.manual_seed(0      )

      __magic_name__						       =    {
          '''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,
      }

      __magic_name__						       =    PriorTransformer(**a__      )
      return model



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

      __magic_name__						       =    {
          '''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,
          ),
      }
      __magic_name__						       =    ShapERenderer(**a__      )
      return model



 def 			snake_case__  (   self		:   Optional[Any]      ):
      __magic_name__						       =    self.dummy_prior
      __magic_name__						       =    self.dummy_image_encoder
      __magic_name__						       =    self.dummy_image_processor
      __magic_name__						       =    self.dummy_renderer

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

      return components



 def 			snake_case__  (   self		:   Optional[Any]			,				a__		:   List[Any]			,				a__		:   Union[str, Any]=0      ):
      __magic_name__						       =    floats_tensor((1, 3, 64, 64)			,				rng=random.Random(a__      )      ).to(a__      )

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



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    '''cpu'''

      __magic_name__						       =    self.get_dummy_components()

      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )

      pipe.set_progress_bar_config(disable=a__      )

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

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

      __magic_name__						       =    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 			snake_case__  (   self		:   Optional[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 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    torch_device == '''cpu'''
      __magic_name__						       =    True
      self._test_inference_batch_single_identical(
          batch_size=2			,				test_max_difference=a__			,				relax_max_difference=a__			,				)



 def 			snake_case__  (   self		:   List[str]      ):
      __magic_name__						       =    self.get_dummy_components()
      __magic_name__						       =    self.pipeline_class(**a__      )
      __magic_name__						       =    pipe.to(a__      )
      pipe.set_progress_bar_config(disable=a__      )

      __magic_name__						       =    1
      __magic_name__						       =    2

      __magic_name__						       =    self.get_dummy_inputs(a__      )

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

      __magic_name__						       =    pipe(**a__			,				num_images_per_prompt=a__      )[0]

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






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



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



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

      __magic_name__						       =    torch.Generator(device=a__      ).manual_seed(0      )

      __magic_name__						       =    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 re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowerCAmelCase         =						object()

# For specifying empty leaf dict `{}`
_lowerCAmelCase         =						object()




def 						UpperCamelCase	(			a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tuple((re.compile(x + '''$'''			) for x in qs)			)
     for i in range(len(a			) - len(a			) + 1			):
          __magic_name__						       =    [x.match(a			) for x, y in zip(a		,						ks[i:]			)]
          if matches and all(a			):
               return True
     return False




def 						UpperCamelCase	(			a			)				->     Tuple:






     '''simple docstring'''
     def replace(a		,						a			):
          for rule, replacement in rules:
               if _match(a		,						a			):
                    return replacement
          return val

     return replace




def 						UpperCamelCase	(			)				->     Union[str, Any]:






     '''simple docstring'''
     return [
         # embeddings
         (("transformer", "wpe", "embedding"), P('''mp'''		,						a			)),
         (("transformer", "wte", "embedding"), P('''mp'''		,						a			)),
         # atention
         (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(a		,						'''mp'''			)),
         (("attention", "out_proj", "kernel"), P('''mp'''		,						a			)),
         (("attention", "out_proj", "bias"), None),
         # mlp
         (("mlp", "c_fc", "kernel"), P(a		,						'''mp'''			)),
         (("mlp", "c_fc", "bias"), P('''mp'''			)),
         (("mlp", "c_proj", "kernel"), P('''mp'''		,						a			)),
         (("mlp", "c_proj", "bias"), None),
         # layer norms
         ((r"ln_\d+", "bias"), None),
         ((r"\d+", r"ln_\d+", "scale"), None),
         (("ln_f", "bias"), None),
         (("ln_f", "scale"), None),
     ]




def 						UpperCamelCase	(			a			)				->     str:






     '''simple docstring'''
     __magic_name__						       =    _get_partition_rules()
     __magic_name__						       =    _replacement_rules(a			)
     __magic_name__						       =    {k: _unmatched for k in flatten_dict(a			)}
     __magic_name__						       =    {k: replace(a		,						a			) for k, v in initd.items()}
     assert _unmatched not in result.values(), "Incomplete partition spec."
     return freeze(unflatten_dict(a			)			)






