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      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Union[str, Any]		=ShapEPipeline
     _SCREAMING_SNAKE_CASE     :     int		=["""prompt"""]
     _SCREAMING_SNAKE_CASE     :     List[str]		=["""prompt"""]
     _SCREAMING_SNAKE_CASE     :     Tuple		=[
         """num_images_per_prompt""",
         """num_inference_steps""",
         """generator""",
         """latents""",
         """guidance_scale""",
         """frame_size""",
         """output_type""",
         """return_dict""",
     ]
     _SCREAMING_SNAKE_CASE     :     List[Any]		=False



     @property
     def 			a__  (					self       ):
            return 32



     @property
     def 			a__  (					self       ):
            return 32



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



     @property
     def 			a__  (					self       ):
            return 8



     @property
     def 			a__  (					self       ):
            _A=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
            return tokenizer



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	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-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
            return CLIPTextModelWithProjection(lowerCAmelCase__       )



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )

            _A=	{
                '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,
            }

            _A=	PriorTransformer(**lowerCAmelCase__       )
            return model



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )

            _A=	{
                '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,
                ),
            }
            _A=	ShapERenderer(**lowerCAmelCase__       )
            return model



     def 			a__  (					self       ):
            _A=	self.dummy_prior
            _A=	self.dummy_text_encoder
            _A=	self.dummy_tokenizer
            _A=	self.dummy_renderer

            _A=	HeunDiscreteScheduler(
                beta_schedule='exp'				,      num_train_timesteps=1024				,      prediction_type='sample'				,      use_karras_sigmas=lowerCAmelCase__				,      clip_sample=lowerCAmelCase__				,      clip_sample_range=1.0				,      )
            _A=	{
                'prior': prior,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'renderer': renderer,
                'scheduler': scheduler,
            }

            return components



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=0       ):
            if str(lowerCAmelCase__       ).startswith('mps'       ):
                   _A=	torch.manual_seed(lowerCAmelCase__       )
            else:
                   _A=	torch.Generator(device=lowerCAmelCase__       ).manual_seed(lowerCAmelCase__       )
            _A=	{
                'prompt': 'horse',
                'generator': generator,
                'num_inference_steps': 1,
                'frame_size': 32,
                'output_type': 'np',
            }
            return inputs



     def 			a__  (					self       ):
            _A=	'cpu'

            _A=	self.get_dummy_components()

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

            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

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

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

            _A=	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 			a__  (					self       ):
            # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
            self._test_inference_batch_consistent(batch_sizes=[1, 2]       )



     def 			a__  (					self       ):
            _A=	torch_device == 'cpu'
            _A=	True

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





     def 			a__  (					self       ):
            _A=	self.get_dummy_components()
            _A=	self.pipeline_class(**lowerCAmelCase__       )
            _A=	pipe.to(lowerCAmelCase__       )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	1
            _A=	2

            _A=	self.get_dummy_inputs(lowerCAmelCase__       )

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

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

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





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



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





     def 			a__  (					self       ):
            _A=	load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/shap_e/test_shap_e_np_out.npy'       )
            _A=	ShapEPipeline.from_pretrained('openai/shap-e'       )
            _A=	pipe.to(lowerCAmelCase__       )
            pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

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

            _A=	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__       )
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase_								=							logging.get_logger(__name__)





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            warnings.warn(
                'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                ' Please use PerceiverImageProcessor instead.'				,      lowerCAmelCase__				,      )
            super().__init__(*lowerCAmelCase__				,      **lowerCAmelCase__       )