




'''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 OwlViTImageProcessor, OwlViTProcessor


@require_vision
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	tempfile.mkdtemp()

           # fmt: off
           UpperCAmelCase     =	['''''', '''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
           UpperCAmelCase     =	dict(zip(lowercase     ,       range(len(lowercase       )       )       )       )
           UpperCAmelCase     =	['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
           UpperCAmelCase     =	{'''unk_token''': '''<unk>'''}

           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']       )
           UpperCAmelCase     =	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(lowercase       ) + '''\n'''       )
           with open(self.merges_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               fp.write('''\n'''.join(lowercase       )       )

           UpperCAmelCase     =	{
               '''do_resize''': True,
               '''size''': 20,
               '''do_center_crop''': True,
               '''crop_size''': 18,
               '''do_normalize''': True,
               '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
               '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
           }
           UpperCAmelCase     =	os.path.join(self.tmpdirname     ,       lowercase       )
           with open(self.image_processor_file     ,       '''w'''     ,       encoding='''utf-8'''       ) as fp:
               json.dump(lowercase     ,       lowercase       )
       def 					A   (			self		:			Optional[int]     ,       **lowercase		:			str       ):



           '''simple docstring'''

           return CLIPTokenizer.from_pretrained(self.tmpdirname     ,       pad_token='''!'''     ,       **lowercase       )
       def 					A   (			self		:			Dict     ,       **lowercase		:			Any       ):



           '''simple docstring'''

           return CLIPTokenizerFast.from_pretrained(self.tmpdirname     ,       pad_token='''!'''     ,       **lowercase       )
       def 					A   (			self		:			Union[str, Any]     ,       **lowercase		:			Dict       ):



           '''simple docstring'''

           return OwlViTImageProcessor.from_pretrained(self.tmpdirname     ,       **lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           shutil.rmtree(self.tmpdirname       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	[np.random.randint(255     ,       size=(3, 30, 400)     ,       dtype=np.uinta       )]

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

           return image_inputs
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_tokenizer()
           UpperCAmelCase     =	self.get_rust_tokenizer()
           UpperCAmelCase     =	self.get_image_processor()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )
           processor_slow.save_pretrained(self.tmpdirname       )
           UpperCAmelCase     =	OwlViTProcessor.from_pretrained(self.tmpdirname     ,       use_fast=lowercase       )

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )
           processor_fast.save_pretrained(self.tmpdirname       )
           UpperCAmelCase     =	OwlViTProcessor.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     ,       lowercase       )
           self.assertIsInstance(processor_fast.tokenizer     ,       lowercase       )

           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     ,       lowercase       )
           self.assertIsInstance(processor_fast.image_processor     ,       lowercase       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=self.get_tokenizer()     ,       image_processor=self.get_image_processor()       )
           processor.save_pretrained(self.tmpdirname       )

           UpperCAmelCase     =	self.get_tokenizer(bos_token='''(BOS)'''     ,       eos_token='''(EOS)'''       )
           UpperCAmelCase     =	self.get_image_processor(do_normalize=lowercase       )

           UpperCAmelCase     =	OwlViTProcessor.from_pretrained(
               self.tmpdirname     ,       bos_token='''(BOS)'''     ,       eos_token='''(EOS)'''     ,       do_normalize=lowercase       )

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

           self.assertEqual(processor.image_processor.to_json_string()     ,       image_processor_add_kwargs.to_json_string()       )
           self.assertIsInstance(processor.image_processor     ,       lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_image_processor()
           UpperCAmelCase     =	self.get_tokenizer()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )

           UpperCAmelCase     =	self.prepare_image_inputs()

           UpperCAmelCase     =	image_processor(lowercase     ,       return_tensors='''np'''       )
           UpperCAmelCase     =	processor(images=lowercase     ,       return_tensors='''np'''       )

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



           '''simple docstring'''

           UpperCAmelCase     =	self.get_image_processor()
           UpperCAmelCase     =	self.get_tokenizer()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )

           UpperCAmelCase     =	'''lower newer'''

           UpperCAmelCase     =	processor(text=lowercase     ,       return_tensors='''np'''       )

           UpperCAmelCase     =	tokenizer(lowercase     ,       return_tensors='''np'''       )

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key][0].tolist()     ,       encoded_processor[key][0].tolist()       )
       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_image_processor()
           UpperCAmelCase     =	self.get_tokenizer()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )

           UpperCAmelCase     =	'''lower newer'''
           UpperCAmelCase     =	self.prepare_image_inputs()

           UpperCAmelCase     =	processor(text=lowercase     ,       images=lowercase       )

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

           # test if it raises when no input is passed
           with pytest.raises(lowercase       ):
               processor()
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''google/owlvit-base-patch32'''
           UpperCAmelCase     =	OwlViTProcessor.from_pretrained(lowercase       )

           UpperCAmelCase     =	['''cat''', '''nasa badge''']
           UpperCAmelCase     =	processor(text=lowercase       )

           UpperCAmelCase     =	16
           self.assertListEqual(list(inputs.keys()       )     ,       ['''input_ids''', '''attention_mask''']       )
           self.assertEqual(inputs['''input_ids'''].shape     ,       (2, seq_length)       )

           # test if it raises when no input is passed
           with pytest.raises(lowercase       ):
               processor()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''google/owlvit-base-patch32'''
           UpperCAmelCase     =	OwlViTProcessor.from_pretrained(lowercase       )

           UpperCAmelCase     =	[['''cat''', '''nasa badge'''], ['''person''']]
           UpperCAmelCase     =	processor(text=lowercase       )

           UpperCAmelCase     =	16
           UpperCAmelCase     =	len(lowercase       )
           UpperCAmelCase     =	max([len(lowercase       ) for texts in input_texts]       )

           self.assertListEqual(list(inputs.keys()       )     ,       ['''input_ids''', '''attention_mask''']       )
           self.assertEqual(inputs['''input_ids'''].shape     ,       (batch_size * num_max_text_queries, seq_length)       )

           # test if it raises when no input is passed
           with pytest.raises(lowercase       ):
               processor()
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	'''google/owlvit-base-patch32'''
           UpperCAmelCase     =	OwlViTProcessor.from_pretrained(lowercase       )

           UpperCAmelCase     =	['''cat''', '''nasa badge''']
           UpperCAmelCase     =	processor(text=lowercase       )

           UpperCAmelCase     =	16
           UpperCAmelCase     =	inputs['''input_ids''']
           UpperCAmelCase     =	[
               [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
               [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
           ]

           self.assertListEqual(list(inputs.keys()       )     ,       ['''input_ids''', '''attention_mask''']       )
           self.assertEqual(inputs['''input_ids'''].shape     ,       (2, seq_length)       )
           self.assertListEqual(list(input_ids[0]       )     ,       predicted_ids[0]       )
           self.assertListEqual(list(input_ids[1]       )     ,       predicted_ids[1]       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_image_processor()
           UpperCAmelCase     =	self.get_tokenizer()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )

           UpperCAmelCase     =	self.prepare_image_inputs()
           UpperCAmelCase     =	self.prepare_image_inputs()

           UpperCAmelCase     =	processor(images=lowercase     ,       query_images=lowercase       )

           self.assertListEqual(list(inputs.keys()       )     ,       ['''query_pixel_values''', '''pixel_values''']       )

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







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_image_processor()
           UpperCAmelCase     =	self.get_tokenizer()

           UpperCAmelCase     =	OwlViTProcessor(tokenizer=lowercase     ,       image_processor=lowercase       )

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

           UpperCAmelCase     =	processor.batch_decode(lowercase       )
           UpperCAmelCase     =	tokenizer.batch_decode(lowercase       )

           self.assertListEqual(lowercase     ,       lowercase       )










'''simple docstring'''







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						(			__a							,							unittest.TestCase				):
       __a       :  Tuple            =      ShapEPipeline
       __a       :  Tuple            =      ["""prompt"""]
       __a       :  Tuple            =      ["""prompt"""]
       __a       :  str            =      [
           """num_images_per_prompt""",
           """num_inference_steps""",
           """generator""",
           """latents""",
           """guidance_scale""",
           """frame_size""",
           """output_type""",
           """return_dict""",
       ]
       __a       :  Union[str, Any]            =      False
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           return 32
       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           return 32
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

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



           '''simple docstring'''

           return 8
       @property
       def 					A   (			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
           return tokenizer
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	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=1_000     ,       )
           return CLIPTextModelWithProjection(lowercase       )
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )

           UpperCAmelCase     =	{
               '''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,
           }

           UpperCAmelCase     =	PriorTransformer(**lowercase       )
           return model
       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )

           UpperCAmelCase     =	{
               '''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,
               ),
           }
           UpperCAmelCase     =	ShapERenderer(**lowercase       )
           return model
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.dummy_prior
           UpperCAmelCase     =	self.dummy_text_encoder
           UpperCAmelCase     =	self.dummy_tokenizer
           UpperCAmelCase     =	self.dummy_renderer

           UpperCAmelCase     =	HeunDiscreteScheduler(
               beta_schedule='''exp'''     ,       num_train_timesteps=1_024     ,       prediction_type='''sample'''     ,       use_karras_sigmas=lowercase     ,       clip_sample=lowercase     ,       clip_sample_range=1.0     ,       )
           UpperCAmelCase     =	{
               '''prior''': prior,
               '''text_encoder''': text_encoder,
               '''tokenizer''': tokenizer,
               '''renderer''': renderer,
               '''scheduler''': scheduler,
           }

           return components
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]=0       ):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase     =	'''cpu'''

           UpperCAmelCase     =	self.get_dummy_components()

           UpperCAmelCase     =	self.pipeline_class(**lowercase       )
           UpperCAmelCase     =	pipe.to(lowercase       )

           pipe.set_progress_bar_config(disable=lowercase       )

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

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

           UpperCAmelCase     =	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		:			int       ):



           '''simple docstring'''

           self._test_inference_batch_consistent(batch_sizes=[1, 2]       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	torch_device == '''cpu'''
           UpperCAmelCase     =	True

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







       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_dummy_components()
           UpperCAmelCase     =	self.pipeline_class(**lowercase       )
           UpperCAmelCase     =	pipe.to(lowercase       )
           pipe.set_progress_bar_config(disable=lowercase       )

           UpperCAmelCase     =	1
           UpperCAmelCase     =	2

           UpperCAmelCase     =	self.get_dummy_inputs(lowercase       )

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

           UpperCAmelCase     =	pipe(**lowercase     ,       num_images_per_prompt=lowercase       )[0]

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






@slow
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			str       ):



           '''simple docstring'''

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







       def 					A   (			self		:			int       ):



           '''simple docstring'''

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

           UpperCAmelCase     =	torch.Generator(device=lowercase       ).manual_seed(0       )

           UpperCAmelCase     =	pipe(
               '''a shark'''     ,       generator=lowercase     ,       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(lowercase     ,       lowercase       )




