



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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     tempfile.mkdtemp()

								A__	  =     BlipImageProcessor()
								A__	  =     BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'       )

								A__	  =     BlipProcessor(_snake_case					,       _snake_case       )

								processor.save_pretrained(self.tmpdirname       )



					def      _a				(  self       :							List[Any]					,       **_snake_case       :							List[str]       ):






								"""simple docstring"""


								return AutoProcessor.from_pretrained(self.tmpdirname					,       **_snake_case       ).tokenizer



					def      _a				(  self       :							Dict					,       **_snake_case       :							List[Any]       ):






								"""simple docstring"""


								return AutoProcessor.from_pretrained(self.tmpdirname					,       **_snake_case       ).image_processor



					def      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname       )



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     [np.random.randint(2_55					,       size=(3, 30, 4_00)					,       dtype=np.uinta       )]

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

								return image_inputs



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     BlipProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

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

								A__	  =     BlipProcessor.from_pretrained(
								    self.tmpdirname					,       bos_token='(BOS)'					,       eos_token='(EOS)'					,       do_normalize=_snake_case					,       padding_value=1.0       )

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

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



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     BlipProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()

								A__	  =     image_processor(_snake_case					,       return_tensors='np'       )
								A__	  =     processor(images=_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      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     BlipProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'

								A__	  =     processor(text=_snake_case       )

								A__	  =     tokenizer(_snake_case					,       return_token_type_ids=_snake_case       )

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



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     BlipProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

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

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



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     BlipProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor.batch_decode(_snake_case       )
								A__	  =     tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     BlipProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

								# 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 random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    A__					=       [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]						)



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionLatentUpscalePipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCamelCase__		     =					frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCamelCase__		     =					frozenset([]  )

     UpperCamelCase__		     =					True




     @property
     def 		snake_case_		(					self):
         A__					=       1
         A__					=       4
         A__					=       (1_6, 1_6)

         A__					=       floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0)).to(a__)
         return image




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             act_fn='''gelu'''			,    attention_head_dim=8			,    norm_num_groups=a__			,    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    time_cond_proj_dim=1_6_0			,    conv_in_kernel=1			,    conv_out_kernel=1			,    cross_attention_dim=3_2			,    down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,    in_channels=8			,    mid_block_type=a__			,    only_cross_attention=a__			,    out_channels=5			,    resnet_time_scale_shift='''scale_shift'''			,    time_embedding_type='''fourier'''			,    timestep_post_act='''gelu'''			,    up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,    )
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    )
         A__					=       EulerDiscreteScheduler(prediction_type='''sample''')
         A__					=       CLIPTextConfig(
             bos_token_id=0			,    eos_token_id=2			,    hidden_size=3_2			,    intermediate_size=3_7			,    layer_norm_eps=1e-05			,    num_attention_heads=4			,    num_hidden_layers=5			,    pad_token_id=1			,    vocab_size=1_0_0_0			,    hidden_act='''quick_gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         A__					=       '''cpu'''

         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)
         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       pipe(**a__).images
         A__					=       image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,    (1, 2_5_6, 2_5_6, 3))
         A__					=       np.array(
             [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
         A__					=       np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(a__			,    1e-3)




     def 		snake_case_		(					self):
         super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_cpu_offload_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_local(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_optional_components(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         A__					=       [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         A__					=       self.get_dummy_inputs(a__)
         A__					=       2

         A__					=       []
         for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                 # no sigma schedulers are not supported
                 # no schedulers
                 continue

             A__					=       getattr(a__			,    scheduler_enum.name)
             A__					=       scheduler_cls.from_config(pipe.scheduler.config)
             A__					=       pipe(**a__)[0]
             outputs.append(a__)

         assert check_same_shape(a__)







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




     def 		snake_case_		(					self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,    torch_dtype=torch.floataa)
         pipe.to('''cuda''')

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         A__					=       pipe(a__			,    generator=a__			,    output_type='''latent''').images

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''')
         assert np.abs((expected_image - image).mean()) < 5e-2




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''')

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''')
         assert np.abs((expected_image - image).max()) < 5e-2
