





'''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    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	tempfile.mkdtemp()

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

         A_     :				str			      =	BlipaProcessor(a__      ,       a__       )

         processor.save_pretrained(self.tmpdirname       )
      def 		_lowerCamelCase      (		self      ,       **a__       ):
         return AutoProcessor.from_pretrained(self.tmpdirname      ,       **a__       ).tokenizer
      def 		_lowerCamelCase      (		self      ,       **a__       ):
         return AutoProcessor.from_pretrained(self.tmpdirname      ,       **a__       ).image_processor
      def 		_lowerCamelCase      (		self       ):
         shutil.rmtree(self.tmpdirname       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	[np.random.randint(255      ,       size=(3, 30, 400)      ,       dtype=np.uinta       )]

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

         return image_inputs
      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	BlipaProcessor(tokenizer=self.get_tokenizer()      ,       image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         A_     :				Dict			      =	self.get_tokenizer(bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""       )
         A_     :				Any			      =	self.get_image_processor(do_normalize=a__      ,       padding_value=1.0       )

         A_     :				Optional[int]			      =	BlipaProcessor.from_pretrained(
             self.tmpdirname      ,       bos_token="""(BOS)"""      ,       eos_token="""(EOS)"""      ,       do_normalize=a__      ,       padding_value=1.0       )

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

         self.assertEqual(processor.image_processor.to_json_string()      ,       image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Any			      =	self.get_image_processor()
         A_     :				int			      =	self.get_tokenizer()

         A_     :				Dict			      =	BlipaProcessor(tokenizer=a__      ,       image_processor=a__       )

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

         A_     :				Any			      =	image_processor(a__      ,       return_tensors="""np"""       )
         A_     :				str			      =	processor(images=a__      ,       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       ):
         A_     :				Tuple			      =	self.get_image_processor()
         A_     :				Dict			      =	self.get_tokenizer()

         A_     :				str			      =	BlipaProcessor(tokenizer=a__      ,       image_processor=a__       )

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

         A_     :				int			      =	processor(text=a__       )

         A_     :				Dict			      =	tokenizer(a__      ,       return_token_type_ids=a__       )

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]      ,       encoded_processor[key]       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	self.get_image_processor()
         A_     :				List[Any]			      =	self.get_tokenizer()

         A_     :				List[str]			      =	BlipaProcessor(tokenizer=a__      ,       image_processor=a__       )

         A_     :				Tuple			      =	"""lower newer"""
         A_     :				Union[str, Any]			      =	self.prepare_image_inputs()

         A_     :				Tuple			      =	processor(text=a__      ,       images=a__       )

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

         # test if it raises when no input is passed
         with pytest.raises(a__       ):
            processor()
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	self.get_image_processor()
         A_     :				Union[str, Any]			      =	self.get_tokenizer()

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

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

         A_     :				Tuple			      =	processor.batch_decode(a__       )
         A_     :				Dict			      =	tokenizer.batch_decode(a__       )

         self.assertListEqual(a__      ,       a__       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	self.get_image_processor()
         A_     :				int			      =	self.get_tokenizer()

         A_     :				Union[str, Any]			      =	BlipaProcessor(tokenizer=a__      ,       image_processor=a__       )

         A_     :				Dict			      =	"""lower newer"""
         A_     :				Any			      =	self.prepare_image_inputs()

         A_     :				List[Any]			      =	processor(text=a__      ,       images=a__       )

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













import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							AltDiffusionPipeline
      a						       =							TEXT_TO_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_BATCH_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      a						       =							TEXT_TO_IMAGE_IMAGE_PARAMS
      def 		_lowerCamelCase      (		self       ):
         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	UNetaDConditionModel(
             block_out_channels=(32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=4      ,       out_channels=4      ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")      ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")      ,       cross_attention_dim=32      ,       )
         A_     :				List[Any]			      =	DDIMScheduler(
             beta_start=0.0_0085      ,       beta_end=0.012      ,       beta_schedule="""scaled_linear"""      ,       clip_sample=a__      ,       set_alpha_to_one=a__      ,       )
         torch.manual_seed(0       )
         A_     :				int			      =	AutoencoderKL(
             block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,       latent_channels=4      ,       )

         # TODO: address the non-deterministic text encoder (fails for save-load tests)
         # torch.manual_seed(0)
         # text_encoder_config = RobertaSeriesConfig(
         #     hidden_size=32,
         #     project_dim=32,
         #     intermediate_size=37,
         #     layer_norm_eps=1e-05,
         #     num_attention_heads=4,
         #     num_hidden_layers=5,
         #     vocab_size=5002,
         # )
         # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

         torch.manual_seed(0       )
         A_     :				Optional[int]			      =	CLIPTextConfig(
             bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=32      ,       projection_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=5002      ,       )
         A_     :				Optional[Any]			      =	CLIPTextModel(a__       )

         A_     :				Union[str, Any]			      =	XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
         A_     :				str			      =	77

         A_     :				Optional[Any]			      =	{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         return components
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__=0       ):
         if str(a__       ).startswith("""mps"""       ):
            A_     :				Union[str, Any]			      =	torch.manual_seed(a__       )
         else:
            A_     :				Dict			      =	torch.Generator(device=a__       ).manual_seed(a__       )
         A_     :				Dict			      =	{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs
      def 		_lowerCamelCase      (		self       ):
         super().test_attention_slicing_forward_pass(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         torch.manual_seed(0       )
         A_     :				List[str]			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				Dict			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder

         A_     :				Tuple			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Tuple			      =	"""A photo of an astronaut"""
         A_     :				Dict			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				Tuple			      =	np.array(
             [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093]       )

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator

         A_     :				Union[str, Any]			      =	self.get_dummy_components()
         A_     :				str			      =	PNDMScheduler(skip_prk_steps=a__       )
         torch.manual_seed(0       )
         A_     :				Any			      =	RobertaSeriesConfig(
             hidden_size=32      ,       project_dim=32      ,       intermediate_size=37      ,       layer_norm_eps=1E-05      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       vocab_size=5002      ,       )
         # TODO: remove after fixing the non-deterministic text encoder
         A_     :				int			      =	RobertaSeriesModelWithTransformation(a__       )
         A_     :				Any			      =	text_encoder
         A_     :				Any			      =	AltDiffusionPipeline(**a__       )
         A_     :				int			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Union[str, Any]			      =	self.get_dummy_inputs(a__       )
         A_     :				Union[str, Any]			      =	alt_pipe(**a__       )
         A_     :				List[str]			      =	output.images
         A_     :				List[Any]			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         A_     :				List[Any]			      =	np.array(
             [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237]       )

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






@slow
@require_torch_gpu
class    _UpperCAmelCase							(		unittest.TestCase     ):
      def 		_lowerCamelCase      (		self       ):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      def 		_lowerCamelCase      (		self       ):
         # make sure here that pndm scheduler skips prk
         A_     :				Any			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       safety_checker=a__       )
         A_     :				Optional[int]			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Tuple			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Tuple			      =	torch.manual_seed(0       )
         A_     :				int			      =	alt_pipe([prompt]      ,       generator=a__      ,       guidance_scale=6.0      ,       num_inference_steps=20      ,       output_type="""np"""       )

         A_     :				Optional[Any]			      =	output.images

         A_     :				int			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				str			      =	np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]       )

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""      ,       subfolder="""scheduler"""       )

         A_     :				int			      =	AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""      ,       scheduler=a__      ,       safety_checker=a__       )
         A_     :				Dict			      =	alt_pipe.to(a__       )
         alt_pipe.set_progress_bar_config(disable=a__       )

         A_     :				Optional[int]			      =	"""A painting of a squirrel eating a burger"""
         A_     :				Any			      =	torch.manual_seed(0       )

         A_     :				List[str]			      =	alt_pipe([prompt]      ,       generator=a__      ,       num_inference_steps=2      ,       output_type="""numpy"""       )
         A_     :				List[Any]			      =	output.images

         A_     :				Tuple			      =	image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         A_     :				Any			      =	np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]       )

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






