



'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class      _lowerCAmelCase							(      unittest.TestCase			):


	def    _a     (self							):
				A_     :						int     =					tempfile.mkdtemp()

				A_     :						Tuple     =					BlipImageProcessor()
				A_     :						Optional[int]     =					GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)

				A_     :						Any     =					BlipaProcessor(lowercase	,				lowercase							)

				processor.save_pretrained(self.tmpdirname							)


	def    _a     (self	,				**lowercase							):
				return AutoProcessor.from_pretrained(self.tmpdirname	,				**lowercase							).tokenizer


	def    _a     (self	,				**lowercase							):
				return AutoProcessor.from_pretrained(self.tmpdirname	,				**lowercase							).image_processor


	def    _a     (self							):
				shutil.rmtree(self.tmpdirname							)


	def    _a     (self							):
				A_     :						List[Any]     =					[np.random.randint(255	,				size=(3, 30, 400)	,				dtype=np.uinta							)]

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

				return image_inputs


	def    _a     (self							):
				A_     :						List[str]     =					BlipaProcessor(tokenizer=self.get_tokenizer()	,				image_processor=self.get_image_processor()							)
				processor.save_pretrained(self.tmpdirname							)

				A_     :						Union[str, Any]     =					self.get_tokenizer(bos_token="""(BOS)"""	,				eos_token="""(EOS)"""							)
				A_     :						Optional[int]     =					self.get_image_processor(do_normalize=lowercase	,				padding_value=1.0							)

				A_     :						Dict     =					BlipaProcessor.from_pretrained(
				    self.tmpdirname	,				bos_token="""(BOS)"""	,				eos_token="""(EOS)"""	,				do_normalize=lowercase	,				padding_value=1.0							)

				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							):
				A_     :						Optional[int]     =					self.get_image_processor()
				A_     :						Optional[int]     =					self.get_tokenizer()

				A_     :						Any     =					BlipaProcessor(tokenizer=lowercase	,				image_processor=lowercase							)

				A_     :						int     =					self.prepare_image_inputs()

				A_     :						Optional[Any]     =					image_processor(lowercase	,				return_tensors="""np"""							)
				A_     :						str     =					processor(images=lowercase	,				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    _a     (self							):
				A_     :						Optional[int]     =					self.get_image_processor()
				A_     :						List[Any]     =					self.get_tokenizer()

				A_     :						Any     =					BlipaProcessor(tokenizer=lowercase	,				image_processor=lowercase							)

				A_     :						List[Any]     =					"""lower newer"""

				A_     :						Tuple     =					processor(text=lowercase							)

				A_     :						Optional[int]     =					tokenizer(lowercase	,				return_token_type_ids=lowercase							)

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


	def    _a     (self							):
				A_     :						Dict     =					self.get_image_processor()
				A_     :						Tuple     =					self.get_tokenizer()

				A_     :						Optional[Any]     =					BlipaProcessor(tokenizer=lowercase	,				image_processor=lowercase							)

				A_     :						Optional[Any]     =					"""lower newer"""
				A_     :						str     =					self.prepare_image_inputs()

				A_     :						Any     =					processor(text=lowercase	,				images=lowercase							)

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

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


	def    _a     (self							):
				A_     :						Optional[int]     =					self.get_image_processor()
				A_     :						Union[str, Any]     =					self.get_tokenizer()

				A_     :						Optional[int]     =					BlipaProcessor(tokenizer=lowercase	,				image_processor=lowercase							)

				A_     :						List[Any]     =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A_     :						Union[str, Any]     =					processor.batch_decode(lowercase							)
				A_     :						Tuple     =					tokenizer.batch_decode(lowercase							)

				self.assertListEqual(lowercase	,				lowercase							)


	def    _a     (self							):
				A_     :						Optional[int]     =					self.get_image_processor()
				A_     :						int     =					self.get_tokenizer()

				A_     :						List[Any]     =					BlipaProcessor(tokenizer=lowercase	,				image_processor=lowercase							)

				A_     :						Any     =					"""lower newer"""
				A_     :						List[Any]     =					self.prepare_image_inputs()

				A_     :						Optional[Any]     =					processor(text=lowercase	,				images=lowercase							)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()							)	,				["""pixel_values""", """input_ids""", """attention_mask"""]							)




'''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      _lowerCAmelCase							(      __UpperCAmelCase				,      unittest.TestCase			):
	__SCREAMING_SNAKE_CASE       :		Union[str, Any]		      = ShapEPipeline
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		str		      = ['prompt']
	__SCREAMING_SNAKE_CASE       :		Dict		      = [
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__SCREAMING_SNAKE_CASE       :		Union[str, 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_     :						Dict     =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
				return tokenizer


	@property
	def    _a     (self							):
				torch.manual_seed(0							)
				A_     :						Any     =					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(lowercase							)


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

				A_     :						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""",
				    """encoder_hid_proj_type""": None,
				    """added_emb_type""": None,
				}

				A_     :						List[str]     =					PriorTransformer(**lowercase							)
				return model


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

				A_     :						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,
				    ),
				}
				A_     :						Dict     =					ShapERenderer(**lowercase							)
				return model


	def    _a     (self							):
				A_     :						Optional[int]     =					self.dummy_prior
				A_     :						Optional[int]     =					self.dummy_text_encoder
				A_     :						int     =					self.dummy_tokenizer
				A_     :						Dict     =					self.dummy_renderer

				A_     :						Tuple     =					HeunDiscreteScheduler(
				    beta_schedule="""exp"""	,				num_train_timesteps=1024	,				prediction_type="""sample"""	,				use_karras_sigmas=lowercase	,				clip_sample=lowercase	,				clip_sample_range=1.0	,				)
				A_     :						Union[str, Any]     =					{
				    """prior""": prior,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """renderer""": renderer,
				    """scheduler""": scheduler,
				}

				return components


	def    _a     (self	,				lowercase	,				lowercase=0							):
				if str(lowercase							).startswith("""mps"""							):
							A_     :						Any     =					torch.manual_seed(lowercase							)
				else:
							A_     :						str     =					torch.Generator(device=lowercase							).manual_seed(lowercase							)
				A_     :						List[str]     =					{
				    """prompt""": """horse""",
				    """generator""": generator,
				    """num_inference_steps""": 1,
				    """frame_size""": 32,
				    """output_type""": """np""",
				}
				return inputs


	def    _a     (self							):
				A_     :						str     =					"""cpu"""

				A_     :						Union[str, Any]     =					self.get_dummy_components()

				A_     :						Optional[int]     =					self.pipeline_class(**lowercase							)
				A_     :						str     =					pipe.to(lowercase							)

				pipe.set_progress_bar_config(disable=lowercase							)

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

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

				A_     :						Tuple     =					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    _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_     :						Tuple     =					torch_device == """cpu"""
				A_     :						Union[str, Any]     =					True

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


	def    _a     (self							):
				A_     :						List[Any]     =					self.get_dummy_components()
				A_     :						Any     =					self.pipeline_class(**lowercase							)
				A_     :						Any     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					1
				A_     :						Union[str, Any]     =					2

				A_     :						Dict     =					self.get_dummy_inputs(lowercase							)

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

				A_     :						List[Any]     =					pipe(**lowercase	,				num_images_per_prompt=lowercase							)[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_     :						Union[str, Any]     =					load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/shap_e/test_shap_e_np_out.npy"""							)
				A_     :						Tuple     =					ShapEPipeline.from_pretrained("""openai/shap-e"""							)
				A_     :						int     =					pipe.to(lowercase							)
				pipe.set_progress_bar_config(disable=lowercase							)

				A_     :						int     =					torch.Generator(device=lowercase							).manual_seed(0							)

				A_     :						List[str]     =					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							)