import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Tuple    			=		StableDiffusionSAGPipeline
 lowerCamelCase	:Dict    			=		TEXT_TO_IMAGE_PARAMS
 lowerCamelCase	:Any    			=		TEXT_TO_IMAGE_BATCH_PARAMS
 lowerCamelCase	:Dict    			=		TEXT_TO_IMAGE_IMAGE_PARAMS
 lowerCamelCase	:Union[str, Any]    			=		TEXT_TO_IMAGE_IMAGE_PARAMS
 lowerCamelCase	:int    			=		False



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        torch.manual_seed(0							)
        _A        =  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        =  DDIMScheduler(
            beta_start=0.0_0085		,				beta_end=0.012		,				beta_schedule="""scaled_linear"""		,				clip_sample=lowerCAmelCase_		,				set_alpha_to_one=lowerCAmelCase_		,				)
        torch.manual_seed(0							)
        _A        =  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		,				)
        torch.manual_seed(0							)
        _A        =  CLIPTextConfig(
            bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
        _A        =  CLIPTextModel(lowerCAmelCase_							)
        _A        =  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

        _A        =  {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=0							)  ->					List[Any]:
        if str(lowerCAmelCase_							).startswith("""mps"""							):
               _A        =  torch.manual_seed(lowerCAmelCase_							)
        else:
               _A        =  torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
        _A        =  {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3							)



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







 """simple docstring"""






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



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""							)
        _A        =  sag_pipe.to(lowerCAmelCase_							)
        sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  """."""
        _A        =  torch.manual_seed(0							)
        _A        =  sag_pipe(
            [prompt]		,				generator=lowerCAmelCase_		,				guidance_scale=7.5		,				sag_scale=1.0		,				num_inference_steps=20		,				output_type="""np"""							)

        _A        =  output.images

        _A        =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A        =  np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-2



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""							)
        _A        =  sag_pipe.to(lowerCAmelCase_							)
        sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  """."""
        _A        =  torch.manual_seed(0							)
        _A        =  sag_pipe(
            [prompt]		,				generator=lowerCAmelCase_		,				guidance_scale=7.5		,				sag_scale=1.0		,				num_inference_steps=20		,				output_type="""np"""							)

        _A        =  output.images

        _A        =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A        =  np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]							)

        assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-2



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""							)
        _A        =  sag_pipe.to(lowerCAmelCase_							)
        sag_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

        _A        =  """."""
        _A        =  torch.manual_seed(0							)
        _A        =  sag_pipe(
            [prompt]		,				width=7_68		,				height=5_12		,				generator=lowerCAmelCase_		,				guidance_scale=7.5		,				sag_scale=1.0		,				num_inference_steps=20		,				output_type="""np"""		,				)

        _A        =  output.images

        assert image.shape == (1, 5_12, 7_68, 3)





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,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



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

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

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

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

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

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

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				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      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

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

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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

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



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

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

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

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




