





'''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					(      snake_case_							,		unittest.TestCase       ):
      _lowercase:      Any											=       ShapEImgaImgPipeline
      _lowercase:      Dict											=       ['''image''']
      _lowercase:      Dict											=       ['''image''']
      _lowercase:      Union[str, Any]											=       [
          '''num_images_per_prompt''',
          '''num_inference_steps''',
          '''generator''',
          '''latents''',
          '''guidance_scale''',
          '''frame_size''',
          '''output_type''',
          '''return_dict''',
      ]
      _lowercase:      Optional[Any]											=       False




      @property
      def     lowercase__   (      self      :			Dict  )    ->				Tuple:
            return 32




      @property
      def     lowercase__   (      self      :			int  )    ->				str:
            return 32




      @property
      def     lowercase__   (      self      :			Optional[Any]  )    ->				str:
            return self.time_input_dim * 4




      @property
      def     lowercase__   (      self      :			Optional[int]  )    ->				int:
            return 8




      @property
      def     lowercase__   (      self      :			int  )    ->				str:
            torch.manual_seed(0  )
            _lowerCAmelCase												=							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												=							CLIPVisionModel(__snake_case  )
            return model




      @property
      def     lowercase__   (      self      :			List[Any]  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							CLIPImageProcessor(
                crop_size=2_24      ,      do_center_crop=__snake_case      ,      do_normalize=__snake_case      ,      do_resize=__snake_case      ,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]      ,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]      ,      resample=3      ,      size=2_24      ,      )

            return image_processor




      @property
      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            torch.manual_seed(0  )

            _lowerCAmelCase												=							{
                """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												=							PriorTransformer(**__snake_case  )
            return model




      @property
      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Optional[Any]:
            torch.manual_seed(0  )

            _lowerCAmelCase												=							{
                """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												=							ShapERenderer(**__snake_case  )
            return model




      def     lowercase__   (      self      :			List[str]  )    ->				List[str]:
            _lowerCAmelCase												=							self.dummy_prior
            _lowerCAmelCase												=							self.dummy_image_encoder
            _lowerCAmelCase												=							self.dummy_image_processor
            _lowerCAmelCase												=							self.dummy_renderer

            _lowerCAmelCase												=							HeunDiscreteScheduler(
                beta_schedule="""exp"""      ,      num_train_timesteps=10_24      ,      prediction_type="""sample"""      ,      use_karras_sigmas=__snake_case      ,      clip_sample=__snake_case      ,      clip_sample_range=1.0      ,      )
            _lowerCAmelCase												=							{
                """prior""": prior,
                """image_encoder""": image_encoder,
                """image_processor""": image_processor,
                """renderer""": renderer,
                """scheduler""": scheduler,
            }

            return components




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			Optional[int]      ,      __snake_case      :			str=0  )    ->				int:
            _lowerCAmelCase												=							floats_tensor((1, 3, 64, 64)      ,      rng=random.Random(__snake_case  )  ).to(__snake_case  )

            if str(__snake_case  ).startswith("""mps"""  ):
                  _lowerCAmelCase												=							torch.manual_seed(__snake_case  )
            else:
                  _lowerCAmelCase												=							torch.Generator(device=__snake_case  ).manual_seed(__snake_case  )
            _lowerCAmelCase												=							{
                """image""": input_image,
                """generator""": generator,
                """num_inference_steps""": 1,
                """frame_size""": 32,
                """output_type""": """np""",
            }
            return inputs




      def     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							"""cpu"""

            _lowerCAmelCase												=							self.get_dummy_components()

            _lowerCAmelCase												=							self.pipeline_class(**__snake_case  )
            _lowerCAmelCase												=							pipe.to(__snake_case  )

            pipe.set_progress_bar_config(disable=__snake_case  )

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

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

            _lowerCAmelCase												=							np.array(
                [
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                    0.00_03_92_16,
                ]  )

            assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2




      def     lowercase__   (      self      :			Any  )    ->				Any:
            # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
            self._test_inference_batch_consistent(batch_sizes=[1, 2]  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[Any]:
            _lowerCAmelCase												=							torch_device == """cpu"""
            _lowerCAmelCase												=							True
            self._test_inference_batch_single_identical(
                batch_size=2      ,      test_max_difference=__snake_case      ,      relax_max_difference=__snake_case      ,      )




      def     lowercase__   (      self      :			Tuple  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.get_dummy_components()
            _lowerCAmelCase												=							self.pipeline_class(**__snake_case  )
            _lowerCAmelCase												=							pipe.to(__snake_case  )
            pipe.set_progress_bar_config(disable=__snake_case  )

            _lowerCAmelCase												=							1
            _lowerCAmelCase												=							2

            _lowerCAmelCase												=							self.get_dummy_inputs(__snake_case  )

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

            _lowerCAmelCase												=							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       ):




      def     lowercase__   (      self      :			Optional[Any]  )    ->				int:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()




      def     lowercase__   (      self      :			Any  )    ->				Optional[int]:
            _lowerCAmelCase												=							load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""  )
            _lowerCAmelCase												=							load_numpy(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                """/shap_e/test_shap_e_img2img_out.npy"""  )
            _lowerCAmelCase												=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""  )
            _lowerCAmelCase												=							pipe.to(__snake_case  )
            pipe.set_progress_bar_config(disable=__snake_case  )

            _lowerCAmelCase												=							torch.Generator(device=__snake_case  ).manual_seed(0  )

            _lowerCAmelCase												=							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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 			UpperCAmelCase					(      unittest.TestCase       ):




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							tempfile.mkdtemp()

            # fmt: off
            _lowerCAmelCase												=							["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
            # fmt: on
            _lowerCAmelCase												=							dict(zip(__snake_case      ,      range(len(__snake_case  )  )  )  )
            _lowerCAmelCase												=							["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
            _lowerCAmelCase												=							{"""unk_token""": """<unk>"""}

            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""vocab_file"""]  )
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES["""merges_file"""]  )
            with open(self.vocab_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write(json.dumps(__snake_case  ) + """\n"""  )
            with open(self.merges_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  fp.write("""\n""".join(__snake_case  )  )

            _lowerCAmelCase												=							{
                """do_resize""": True,
                """size""": 20,
                """do_center_crop""": True,
                """crop_size""": 18,
                """do_normalize""": True,
                """image_mean""": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                """image_std""": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
            }
            _lowerCAmelCase												=							os.path.join(self.tmpdirname      ,      __snake_case  )
            with open(self.image_processor_file      ,      """w"""      ,      encoding="""utf-8"""  ) as fp:
                  json.dump(__snake_case      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      **__snake_case      :			Dict  )    ->				List[str]:
            return CLIPTokenizer.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      **__snake_case      :			Any  )    ->				List[Any]:
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      **__snake_case      :			List[str]  )    ->				int:
            return ViTImageProcessor.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			str  )    ->				Optional[Any]:
            shutil.rmtree(self.tmpdirname  )




      def     lowercase__   (      self      :			str  )    ->				List[Any]:
            _lowerCAmelCase												=							[np.random.randint(2_55      ,      size=(3, 30, 4_00)      ,      dtype=np.uinta  )]

            _lowerCAmelCase												=							[Image.fromarray(np.moveaxis(__snake_case      ,      0      ,      -1  )  ) for x in image_inputs]

            return image_inputs




      def     lowercase__   (      self      :			Optional[Any]  )    ->				List[str]:
            _lowerCAmelCase												=							self.get_tokenizer()
            _lowerCAmelCase												=							self.get_rust_tokenizer()
            _lowerCAmelCase												=							self.get_image_processor()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_slow.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname      ,      use_fast=__snake_case  )

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )
            processor_fast.save_pretrained(self.tmpdirname  )
            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(self.tmpdirname  )

            self.assertEqual(processor_slow.tokenizer.get_vocab()      ,      tokenizer_slow.get_vocab()  )
            self.assertEqual(processor_fast.tokenizer.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertEqual(tokenizer_slow.get_vocab()      ,      tokenizer_fast.get_vocab()  )
            self.assertIsInstance(processor_slow.tokenizer      ,      __snake_case  )
            self.assertIsInstance(processor_fast.tokenizer      ,      __snake_case  )

            self.assertEqual(processor_slow.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertEqual(processor_fast.image_processor.to_json_string()      ,      image_processor.to_json_string()  )
            self.assertIsInstance(processor_slow.image_processor      ,      __snake_case  )
            self.assertIsInstance(processor_fast.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Dict:
            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

            _lowerCAmelCase												=							self.get_tokenizer(bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""  )
            _lowerCAmelCase												=							self.get_image_processor(do_normalize=__snake_case      ,      padding_value=1.0  )

            _lowerCAmelCase												=							CLIPSegProcessor.from_pretrained(
                self.tmpdirname      ,      bos_token="""(BOS)"""      ,      eos_token="""(EOS)"""      ,      do_normalize=__snake_case      ,      padding_value=1.0  )

            self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()  )
            self.assertIsInstance(processor.tokenizer      ,      __snake_case  )

            self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()  )
            self.assertIsInstance(processor.image_processor      ,      __snake_case  )




      def     lowercase__   (      self      :			Tuple  )    ->				List[Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							image_processor(__snake_case      ,      return_tensors="""np"""  )
            _lowerCAmelCase												=							processor(images=__snake_case      ,      return_tensors="""np"""  )

            for key in input_feat_extract.keys():
                  self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2  )




      def     lowercase__   (      self      :			Any  )    ->				str:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""

            _lowerCAmelCase												=							processor(text=__snake_case  )

            _lowerCAmelCase												=							tokenizer(__snake_case  )

            for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]  )




      def     lowercase__   (      self      :			str  )    ->				Union[str, Any]:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							"""lower newer"""
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(text=__snake_case      ,      images=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""input_ids""", """attention_mask""", """pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							self.prepare_image_inputs()
            _lowerCAmelCase												=							self.prepare_image_inputs()

            _lowerCAmelCase												=							processor(images=__snake_case      ,      visual_prompt=__snake_case  )

            self.assertListEqual(list(inputs.keys()  )      ,      ["""pixel_values""", """conditional_pixel_values"""]  )

            # test if it raises when no input is passed
            with pytest.raises(__snake_case  ):
                  processor()




      def     lowercase__   (      self      :			int  )    ->				Tuple:
            _lowerCAmelCase												=							self.get_image_processor()
            _lowerCAmelCase												=							self.get_tokenizer()

            _lowerCAmelCase												=							CLIPSegProcessor(tokenizer=__snake_case      ,      image_processor=__snake_case  )

            _lowerCAmelCase												=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _lowerCAmelCase												=							processor.batch_decode(__snake_case  )
            _lowerCAmelCase												=							tokenizer.batch_decode(__snake_case  )

            self.assertListEqual(__snake_case      ,      __snake_case  )






