




'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	tempfile.mkdtemp()

            # fmt: off
            __snake_case            =	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
            # fmt: on
            __snake_case            =	os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['''vocab_file''']	)
            with open(self.vocab_file     ,	'''w'''     ,	encoding='''utf-8'''	) as vocab_writer:
                 vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)

            __snake_case            =	{
                '''do_resize''': True,
                '''size''': {'''height''': 18, '''width''': 18},
                '''do_normalize''': True,
                '''image_mean''': [0.5, 0.5, 0.5],
                '''image_std''': [0.5, 0.5, 0.5],
            }
            __snake_case            =	os.path.join(self.tmpdirname     ,	__SCREAMING_SNAKE_CASE	)
            with open(self.image_processor_file     ,	'''w'''     ,	encoding='''utf-8'''	) as fp:
                 json.dump(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	int:



            '''simple docstring'''




            return BertTokenizer.from_pretrained(self.tmpdirname     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




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

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

            return image_inputs


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_image_processor()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            processor.save_pretrained(self.tmpdirname	)
            __snake_case            =	VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname	)

            self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()	)
            self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)	)

            self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()	)
            self.assertIsInstance(processor.image_processor     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	VisionTextDualEncoderProcessor(
                tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()	)
            processor.save_pretrained(self.tmpdirname	)

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

            __snake_case            =	VisionTextDualEncoderProcessor.from_pretrained(
                self.tmpdirname     ,	bos_token='''(BOS)'''     ,	eos_token='''(EOS)'''     ,	do_normalize=__SCREAMING_SNAKE_CASE     ,	padding_value=1.0	)

            self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()	)
            self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)	)

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


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.prepare_image_inputs()

            __snake_case            =	image_processor(__SCREAMING_SNAKE_CASE     ,	return_tensors='''np'''	)
            __snake_case            =	processor(images=__SCREAMING_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    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	'''lower newer'''

            __snake_case            =	processor(text=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer(__SCREAMING_SNAKE_CASE	)

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


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

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

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

            # test if it raises when no input is passed
            with self.assertRaises(__SCREAMING_SNAKE_CASE	):
                 processor()


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

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

            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.get_image_processor()
            __snake_case            =	self.get_tokenizer()

            __snake_case            =	VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,	image_processor=__SCREAMING_SNAKE_CASE	)

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

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

            self.assertListEqual(list(inputs.keys()	)     ,	processor.model_input_names	)







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_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     ShapEPipeline
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     ["prompt"]
							UpperCAmelCase				   =     [
							    "num_images_per_prompt",
							    "num_inference_steps",
							    "generator",
							    "latents",
							    "guidance_scale",
							    "frame_size",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase				   =     False







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







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







							@property
							def  UpperCamelCase_						(   self      :     List[str]		):
												return self.time_input_dim * 4







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







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







							@property
							def  UpperCamelCase_						(   self      :     List[Any]		):
												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=1000 ,			)
												return CLIPTextModelWithProjection(_A		)







							@property
							def  UpperCamelCase_						(   self      :     int		):
												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(**_A		)
												return model







							@property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												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(**_A		)
												return model







							def  UpperCamelCase_						(   self      :     str		):
												_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=1024 ,			prediction_type='''sample''' ,			use_karras_sigmas=_A ,			clip_sample=_A ,			clip_sample_range=1.0 ,			)
												_UpperCamelCase			     =     {
												    '''prior''': prior,
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''renderer''': renderer,
												    '''scheduler''': scheduler,
												}

												return components







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     Tuple ,			_A      :     Optional[int]=0		):
												if str(_A		).startswith('''mps'''		):
																	_UpperCamelCase			     =     torch.manual_seed(_A		)
												else:
																	_UpperCamelCase			     =     torch.Generator(device=_A		).manual_seed(_A		)
												_UpperCamelCase			     =     {
												    '''prompt''': '''horse''',
												    '''generator''': generator,
												    '''num_inference_steps''': 1,
												    '''frame_size''': 32,
												    '''output_type''': '''np''',
												}
												return inputs







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     '''cpu'''

												_UpperCamelCase			     =     self.get_dummy_components()

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

												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     pipe(**self.get_dummy_inputs(_A		)		)
												_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  UpperCamelCase_						(   self      :     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  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     torch_device == '''cpu'''
												_UpperCamelCase			     =     True

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     self.get_dummy_components()
												_UpperCamelCase			     =     self.pipeline_class(**_A		)
												_UpperCamelCase			     =     pipe.to(_A		)
												pipe.set_progress_bar_config(disable=_A		)

												_UpperCamelCase			     =     1
												_UpperCamelCase			     =     2

												_UpperCamelCase			     =     self.get_dummy_inputs(_A		)

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

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

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




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







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







							def  UpperCamelCase_						(   self      :     List[str]		):
												_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(_A		)
												pipe.set_progress_bar_config(disable=_A		)

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

												_UpperCamelCase			     =     pipe(
												    '''a shark''' ,			generator=_A ,			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(_A ,			_A		)
