


'''simple docstring'''







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 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     ShapEImgaImgPipeline
     __magic_name__           =     ["image"]
     __magic_name__           =     ["image"]
     __magic_name__           =     [
         "num_images_per_prompt",
         "num_inference_steps",
         "generator",
         "latents",
         "guidance_scale",
         "frame_size",
         "output_type",
         "return_dict",
     ]
     __magic_name__           =     False







     @property
     def a						(			self ):

            '''simple docstring'''

            return 32







     @property
     def a						(			self ):

            '''simple docstring'''

            return 32







     @property
     def a						(			self ):

            '''simple docstring'''

            return self.time_input_dim * 4







     @property
     def a						(			self ):

            '''simple docstring'''

            return 8







     @property
     def a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )
            _lowerCAmelCase     :				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   ,						)

            _lowerCAmelCase     :				List[str]       =							CLIPVisionModel(snake_case__ )
            return model







     @property
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							CLIPImageProcessor(
                crop_size=224   ,						do_center_crop=snake_case__   ,						do_normalize=snake_case__   ,						do_resize=snake_case__   ,						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 a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )

            _lowerCAmelCase     :				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',
                'embedding_proj_norm_type': 'layer',
                'encoder_hid_proj_type': None,
                'added_emb_type': None,
            }

            _lowerCAmelCase     :				str       =							PriorTransformer(**snake_case__ )
            return model







     @property
     def a						(			self ):

            '''simple docstring'''

            torch.manual_seed(0 )

            _lowerCAmelCase     :				List[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     :				str       =							ShapERenderer(**snake_case__ )
            return model







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.dummy_prior
            _lowerCAmelCase     :				str       =							self.dummy_image_encoder
            _lowerCAmelCase     :				str       =							self.dummy_image_processor
            _lowerCAmelCase     :				str       =							self.dummy_renderer

            _lowerCAmelCase     :				List[str]       =							HeunDiscreteScheduler(
                beta_schedule='exp'   ,						num_train_timesteps=1024   ,						prediction_type='sample'   ,						use_karras_sigmas=snake_case__   ,						clip_sample=snake_case__   ,						clip_sample_range=1.0   ,						)
            _lowerCAmelCase     :				Optional[Any]       =							{
                'prior': prior,
                'image_encoder': image_encoder,
                'image_processor': image_processor,
                'renderer': renderer,
                'scheduler': scheduler,
            }

            return components







     def a						(			self   ,						snake_case__   ,						snake_case__=0 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(snake_case__ ) ).to(snake_case__ )

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				int       =							'cpu'

            _lowerCAmelCase     :				int       =							self.get_dummy_components()

            _lowerCAmelCase     :				Optional[int]       =							self.pipeline_class(**snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							pipe.to(snake_case__ )

            pipe.set_progress_bar_config(disable=snake_case__ )

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

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

            _lowerCAmelCase     :				Tuple       =							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 a						(			self ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							torch_device == 'cpu'
            _lowerCAmelCase     :				Optional[int]       =							True
            self._test_inference_batch_single_identical(
                batch_size=2   ,						test_max_difference=snake_case__   ,						relax_max_difference=snake_case__   ,						)







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_dummy_components()
            _lowerCAmelCase     :				Optional[Any]       =							self.pipeline_class(**snake_case__ )
            _lowerCAmelCase     :				Any       =							pipe.to(snake_case__ )
            pipe.set_progress_bar_config(disable=snake_case__ )

            _lowerCAmelCase     :				Tuple       =							1
            _lowerCAmelCase     :				Any       =							2

            _lowerCAmelCase     :				Optional[int]       =							self.get_dummy_inputs(snake_case__ )

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

            _lowerCAmelCase     :				Dict       =							pipe(**snake_case__   ,						num_images_per_prompt=snake_case__ )[0]

            assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

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







     def a						(			self ):

            '''simple docstring'''

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

            _lowerCAmelCase     :				Tuple       =							torch.Generator(device=snake_case__ ).manual_seed(0 )

            _lowerCAmelCase     :				Any       =							pipe(
                snake_case__   ,						generator=snake_case__   ,						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(snake_case__   ,						snake_case__ )





'''simple docstring'''







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

