





from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class      _SCREAMING_SNAKE_CASE		(			UpperCamelCase_					):







      '''simple docstring'''
      lowercase_	=    """philschmid/bart-large-cnn-samsum"""
      lowercase_	=    (
          """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
          """and returns a summary of the text."""
      )
      lowercase_	=    """summarizer"""
      lowercase_	=    AutoTokenizer
      lowercase_	=    AutoModelForSeqaSeqLM

      lowercase_	=    ["""text"""]
      lowercase_	=    ["""text"""]

      def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				UpperCAmelCase_       :     Dict) ->str:



        '''simple docstring'''



        return self.pre_processor(_a ,				return_tensors="pt" ,				truncation=_a)

      def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple ,				UpperCAmelCase_       :     Union[str, Any]) ->Any:



        '''simple docstring'''



        return self.model.generate(**_a)[0]







      def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     Optional[Any]) ->int:



        '''simple docstring'''



        return self.pre_processor.decode(_a ,				skip_special_tokens=_a ,				clean_up_tokenization_spaces=_a)








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		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    ShapEImgaImgPipeline
 lowercase_	=    ["image"]
 lowercase_	=    ["image"]
 lowercase_	=    [
     "num_images_per_prompt",
     "num_inference_steps",
     "generator",
     "latents",
     "guidance_scale",
     "frame_size",
     "output_type",
     "return_dict",
 ]
 lowercase_	=    False

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Union[str, Any]:



   '''simple docstring'''



   return 32

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



   '''simple docstring'''



   return 32

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->List[Any]:



   '''simple docstring'''



   return self.time_input_dim * 4

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Union[str, Any]:



   '''simple docstring'''



   return 8

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->str:



   '''simple docstring'''



   torch.manual_seed(0)
   lowerCamelCase__:   List[str]    						=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 ,				)

   lowerCamelCase__:   Union[str, Any]    						=CLIPVisionModel(UpperCAmelCase_)
   return model

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



   '''simple docstring'''



   lowerCamelCase__:   str    						=CLIPImageProcessor(
       crop_size=224 ,				do_center_crop=UpperCAmelCase_ ,				do_normalize=UpperCAmelCase_ ,				do_resize=UpperCAmelCase_ ,				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 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->Optional[int]:



   '''simple docstring'''



   torch.manual_seed(0)

   lowerCamelCase__:   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,
   }

   lowerCamelCase__:   Optional[int]    						=PriorTransformer(**UpperCAmelCase_)
   return model

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Dict:



   '''simple docstring'''



   torch.manual_seed(0)

   lowerCamelCase__:   Tuple    						={
       "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,
       ),
   }
   lowerCamelCase__:   Any    						=ShapERenderer(**UpperCAmelCase_)
   return model

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.dummy_prior
   lowerCamelCase__:   str    						=self.dummy_image_encoder
   lowerCamelCase__:   Any    						=self.dummy_image_processor
   lowerCamelCase__:   Dict    						=self.dummy_renderer

   lowerCamelCase__:   Tuple    						=HeunDiscreteScheduler(
       beta_schedule="exp" ,				num_train_timesteps=1_024 ,				prediction_type="sample" ,				use_karras_sigmas=UpperCAmelCase_ ,				clip_sample=UpperCAmelCase_ ,				clip_sample_range=1.0 ,				)
   lowerCamelCase__:   Tuple    						={
       "prior": prior,
       "image_encoder": image_encoder,
       "image_processor": image_processor,
       "renderer": renderer,
       "scheduler": scheduler,
   }

   return components

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     Union[str, Any] ,				UpperCAmelCase_       :     Any=0) ->int:



   '''simple docstring'''



   lowerCamelCase__:   int    						=floats_tensor((1, 3, 64, 64) ,				rng=random.Random(UpperCAmelCase_)).to(UpperCAmelCase_)

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

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[Any]    						="cpu"

   lowerCamelCase__:   Optional[Any]    						=self.get_dummy_components()

   lowerCamelCase__:   str    						=self.pipeline_class(**UpperCAmelCase_)
   lowerCamelCase__:   Optional[int]    						=pipe.to(UpperCAmelCase_)

   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

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

   lowerCamelCase__:   Any    						=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 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Dict:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   str    						=torch_device == "cpu"
   lowerCamelCase__:   str    						=True
   self._test_inference_batch_single_identical(
       batch_size=2 ,				test_max_difference=UpperCAmelCase_ ,				relax_max_difference=UpperCAmelCase_ ,				)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->str:



   '''simple docstring'''



   lowerCamelCase__:   Any    						=self.get_dummy_components()
   lowerCamelCase__:   List[str]    						=self.pipeline_class(**UpperCAmelCase_)
   lowerCamelCase__:   Union[str, Any]    						=pipe.to(UpperCAmelCase_)
   pipe.set_progress_bar_config(disable=UpperCAmelCase_)

   lowerCamelCase__:   Union[str, Any]    						=1
   lowerCamelCase__:   List[str]    						=2

   lowerCamelCase__:   int    						=self.get_dummy_inputs(UpperCAmelCase_)

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

   lowerCamelCase__:   List[str]    						=pipe(**UpperCAmelCase_ ,				num_images_per_prompt=UpperCAmelCase_)[0]

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



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







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_      (self       :     str) ->Optional[int]:



   '''simple docstring'''



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







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int]) ->Optional[int]:



   '''simple docstring'''



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

   lowerCamelCase__:   int    						=torch.Generator(device=UpperCAmelCase_).manual_seed(0)

   lowerCamelCase__:   Union[str, Any]    						=pipe(
       UpperCAmelCase_ ,				generator=UpperCAmelCase_ ,				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(UpperCAmelCase_ ,				UpperCAmelCase_)

