


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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					(			UpperCAmelCase__     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Dict						      =	ShapEPipeline
     UpperCAmelCase__ :  List[Any]						      =	["prompt"]
     UpperCAmelCase__ :  Union[str, Any]						      =	["prompt"]
     UpperCAmelCase__ :  str						      =	[
         "num_images_per_prompt",
         "num_inference_steps",
         "generator",
         "latents",
         "guidance_scale",
         "frame_size",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__ :  Optional[Any]						      =	False





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





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return 32





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return self.time_input_dim * 4





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return 8





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				str     =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
        return tokenizer





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)
        __lowerCamelCase:				Optional[Any]     =	CLIPTextConfig(
            bos_token_id=0    ,	eos_token_id=2    ,	hidden_size=self.text_embedder_hidden_size    ,	projection_dim=self.text_embedder_hidden_size    ,	intermediate_size=37    ,	layer_norm_eps=1E-0_5    ,	num_attention_heads=4    ,	num_hidden_layers=5    ,	pad_token_id=1    ,	vocab_size=1000    ,	)
        return CLIPTextModelWithProjection(lowerCamelCase__							)





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        torch.manual_seed(0							)

        __lowerCamelCase:				Union[str, Any]     =	{
            "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",
            "encoder_hid_proj_type": None,
            "added_emb_type": None,
        }

        __lowerCamelCase:				int     =	PriorTransformer(**lowerCamelCase__							)
        return model





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        torch.manual_seed(0							)

        __lowerCamelCase:				str     =	{
            "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:				int     =	ShapERenderer(**lowerCamelCase__							)
        return model





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				int     =	self.dummy_prior
        __lowerCamelCase:				Optional[Any]     =	self.dummy_text_encoder
        __lowerCamelCase:				Optional[Any]     =	self.dummy_tokenizer
        __lowerCamelCase:				Dict     =	self.dummy_renderer

        __lowerCamelCase:				Tuple     =	HeunDiscreteScheduler(
            beta_schedule="""exp"""    ,	num_train_timesteps=1024    ,	prediction_type="""sample"""    ,	use_karras_sigmas=lowerCamelCase__    ,	clip_sample=lowerCamelCase__    ,	clip_sample_range=1.0    ,	)
        __lowerCamelCase:				Optional[int]     =	{
            "prior": prior,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "renderer": renderer,
            "scheduler": scheduler,
        }

        return components





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0							):







        if str(lowerCamelCase__							).startswith("""mps"""							):
           __lowerCamelCase:				Dict     =	torch.manual_seed(lowerCamelCase__							)
        else:
           __lowerCamelCase:				int     =	torch.Generator(device=lowerCamelCase__							).manual_seed(lowerCamelCase__							)
        __lowerCamelCase:				Any     =	{
            "prompt": "horse",
            "generator": generator,
            "num_inference_steps": 1,
            "frame_size": 32,
            "output_type": "np",
        }
        return inputs





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	"cpu"

        __lowerCamelCase:				Dict     =	self.get_dummy_components()

        __lowerCamelCase:				Optional[Any]     =	self.pipeline_class(**lowerCamelCase__							)
        __lowerCamelCase:				Optional[Any]     =	pipe.to(lowerCamelCase__							)

        pipe.set_progress_bar_config(disable=lowerCamelCase__							)

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

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

        __lowerCamelCase:				Optional[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					:				Tuple							):
        self._test_inference_batch_consistent(batch_sizes=[1, 2]							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	torch_device == "cpu"
        __lowerCamelCase:				int     =	True

        self._test_inference_batch_single_identical(
            batch_size=2    ,	test_max_difference=lowerCamelCase__    ,	relax_max_difference=lowerCamelCase__    ,	)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				int     =	self.get_dummy_components()
        __lowerCamelCase:				Union[str, Any]     =	self.pipeline_class(**lowerCamelCase__							)
        __lowerCamelCase:				str     =	pipe.to(lowerCamelCase__							)
        pipe.set_progress_bar_config(disable=lowerCamelCase__							)

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

        __lowerCamelCase:				int     =	self.get_dummy_inputs(lowerCamelCase__							)

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

        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Optional[int]     =	load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/shap_e/test_shap_e_np_out.npy"""							)
        __lowerCamelCase:				Optional[Any]     =	ShapEPipeline.from_pretrained("""openai/shap-e"""							)
        __lowerCamelCase:				Dict     =	pipe.to(lowerCamelCase__							)
        pipe.set_progress_bar_config(disable=lowerCamelCase__							)

        __lowerCamelCase:				Dict     =	torch.Generator(device=lowerCamelCase__							).manual_seed(0							)

        __lowerCamelCase:				Optional[Any]     =	pipe(
            """a shark"""    ,	generator=lowerCamelCase__    ,	guidance_scale=15.0    ,	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__							)






_A		:				Tuple									=       '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				Optional[int]     =	input("""Enter message: """ )
   __lowerCamelCase:				Dict     =	input("""Enter key [alphanumeric]: """ )
   __lowerCamelCase:				List[Any]     =	input("""Encrypt/Decrypt [e/d]: """ )

   if mode.lower().startswith("""e""" ):
      __lowerCamelCase:				Optional[int]     =	"""encrypt"""
      __lowerCamelCase:				Optional[int]     =	encrypt_message(snake_case						,      snake_case )
   elif mode.lower().startswith("""d""" ):
      __lowerCamelCase:				Union[str, Any]     =	"""decrypt"""
      __lowerCamelCase:				Optional[Any]     =	decrypt_message(snake_case						,      snake_case )

   print(f'\n{mode.title()}ed message:' )
   print(snake_case )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """encrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str )       ->							str:
   return translate_message(snake_case						,      snake_case						,      """decrypt""" )



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    str						,      snake_case	:    str )       ->							str:
   __lowerCamelCase:				Any     =	[]
   __lowerCamelCase:				Optional[int]     =	0
   __lowerCamelCase:				Any     =	key.upper()

   for symbol in message:
      __lowerCamelCase:				int     =	LETTERS.find(symbol.upper() )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index] )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index] )

         num %= len(snake_case )

         if symbol.isupper():
            translated.append(LETTERS[num] )
         elif symbol.islower():
            translated.append(LETTERS[num].lower() )

         key_index += 1
         if key_index == len(snake_case ):
            __lowerCamelCase:				Union[str, Any]     =	0
      else:
         translated.append(snake_case )
   return "".join(snake_case )


if __name__ == "__main__":
   main()


