






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 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Tuple									=      ShapEImgaImgPipeline
					_A						:			Optional[int]									=      ['''image''']
					_A						:			List[Any]									=      ['''image''']
					_A						:			str									=      [
					    '''num_images_per_prompt''',
					    '''num_inference_steps''',
					    '''generator''',
					    '''latents''',
					    '''guidance_scale''',
					    '''frame_size''',
					    '''output_type''',
					    '''return_dict''',
					]
					_A						:			int									=      False



					@property
					def       lowerCAmelCase				(  self				:	List[str]							)			->  int:


												"""simple docstring"""

												return 32



					@property
					def       lowerCAmelCase				(  self				:	int							)			->  Any:


												"""simple docstring"""

												return 32



					@property
					def       lowerCAmelCase				(  self				:	Dict							)			->  Optional[int]:


												"""simple docstring"""

												return self.time_input_dim * 4



					@property
					def       lowerCAmelCase				(  self				:	List[Any]							)			->  List[str]:


												"""simple docstring"""

												return 8



					@property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  Optional[Any]:


												"""simple docstring"""

												torch.manual_seed(0							)
												__lowercase      :							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		,     )

												__lowercase      :							Union[str, Any]				=     CLIPVisionModel(__a							)
												return model



					@property
					def       lowerCAmelCase				(  self				:	List[str]							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	Optional[int]							)			->  Optional[int]:


												"""simple docstring"""

												torch.manual_seed(0							)

												__lowercase      :							List[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,
												}

												__lowercase      :							str				=     PriorTransformer(**__a							)
												return model



					@property
					def       lowerCAmelCase				(  self				:	Dict							)			->  int:


												"""simple docstring"""

												torch.manual_seed(0							)

												__lowercase      :							Optional[Any]				=     {
												    """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,
												    ),
												}
												__lowercase      :							Any				=     ShapERenderer(**__a							)
												return model



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Any:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.dummy_prior
												__lowercase      :							Optional[int]				=     self.dummy_image_encoder
												__lowercase      :							Any				=     self.dummy_image_processor
												__lowercase      :							Optional[int]				=     self.dummy_renderer

												__lowercase      :							str				=     HeunDiscreteScheduler(
												    beta_schedule="""exp"""		,     num_train_timesteps=1024		,     prediction_type="""sample"""		,     use_karras_sigmas=__a		,     clip_sample=__a		,     clip_sample_range=1.0		,     )
												__lowercase      :							List[str]				=     {
												    """prior""": prior,
												    """image_encoder""": image_encoder,
												    """image_processor""": image_processor,
												    """renderer""": renderer,
												    """scheduler""": scheduler,
												}

												return components



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	int		,     __a				:	str=0							)			->  int:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     floats_tensor((1, 3, 64, 64)		,     rng=random.Random(__a							)							).to(__a							)

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							str				=     """cpu"""

												__lowercase      :							Any				=     self.get_dummy_components()

												__lowercase      :							int				=     self.pipeline_class(**__a							)
												__lowercase      :							Union[str, Any]				=     pipe.to(__a							)

												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							str				=     pipe(**self.get_dummy_inputs(__a							)							)
												__lowercase      :							List[str]				=     output.images[0]
												__lowercase      :							Optional[int]				=     image[0, -3:, -3:, -1]

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

												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	int							)			->  str:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Any							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     torch_device == """cpu"""
												__lowercase      :							Tuple				=     True
												self._test_inference_batch_single_identical(
												    batch_size=2		,     test_max_difference=__a		,     relax_max_difference=__a		,     )



					def       lowerCAmelCase				(  self				:	str							)			->  Any:


												"""simple docstring"""

												__lowercase      :							int				=     self.get_dummy_components()
												__lowercase      :							Any				=     self.pipeline_class(**__a							)
												__lowercase      :							List[str]				=     pipe.to(__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							Tuple				=     1
												__lowercase      :							Dict				=     2

												__lowercase      :							Dict				=     self.get_dummy_inputs(__a							)

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

												__lowercase      :							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 		lowerCAmelCase     ( unittest.TestCase			):

					'''simple docstring'''

					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""							)
												__lowercase      :							Optional[Any]				=     load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/shap_e/test_shap_e_img2img_out.npy"""							)
												__lowercase      :							str				=     ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""							)
												__lowercase      :							str				=     pipe.to(__a							)
												pipe.set_progress_bar_config(disable=__a							)

												__lowercase      :							Any				=     torch.Generator(device=__a							).manual_seed(0							)

												__lowercase      :							Dict				=     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							)







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)