






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   UpperCamelCase  (       snake_case__     :   Optional[int]  ):




     '''simple docstring'''






     __snake_case			:Dict          =						[tensor.shape for tensor in tensor_list]
     return all(shape == shapes[0] for shape in shapes[1:]  )




class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Union[str, Any] 				= StableDiffusionLatentUpscalePipeline
       lowerCamelCase							:				Dict 				= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       lowerCamelCase							:				int 				= PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       lowerCamelCase							:				Tuple 				= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCamelCase							:				str 				= frozenset(
           [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       lowerCamelCase							:				List[Any] 				= frozenset([])

       lowerCamelCase							:				List[Any] 				= True



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:int          =						1
            __snake_case			:Dict          =						4
            __snake_case			:List[str]          =						(16, 16)

            __snake_case			:List[str]          =						floats_tensor((batch_size, num_channels) + sizes			,						rng=random.Random(0    )    ).to(a__    )
            return image



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






            '''simple docstring'''





            torch.manual_seed(0    )
            __snake_case			:List[Any]          =						UNetaDConditionModel(
                act_fn="""gelu"""			,						attention_head_dim=8			,						norm_num_groups=a__			,						block_out_channels=[32, 32, 64, 64]			,						time_cond_proj_dim=1_60			,						conv_in_kernel=1			,						conv_out_kernel=1			,						cross_attention_dim=32			,						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""")			,						)
            __snake_case			:Dict          =						AutoencoderKL(
                block_out_channels=[32, 32, 64, 64]			,						in_channels=3			,						out_channels=3			,						down_block_types=[
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                    """DownEncoderBlock2D""",
                ]			,						up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,						latent_channels=4			,						)
            __snake_case			:Dict          =						EulerDiscreteScheduler(prediction_type="""sample"""    )
            __snake_case			:Any          =						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			,						hidden_act="""quick_gelu"""			,						projection_dim=5_12			,						)
            __snake_case			:str          =						CLIPTextModel(a__    )
            __snake_case			:Tuple          =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

            __snake_case			:Optional[int]          =						{
                """unet""": model.eval(),
                """vae""": vae.eval(),
                """scheduler""": scheduler,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
            }

            return components



       def 					__lowercase						(							self			,						a__			,						a__=0    ) ->      List[Any]:






            '''simple docstring'''





            if str(a__    ).startswith("""mps"""    ):
                 __snake_case			:Dict          =						torch.manual_seed(a__    )
            else:
                 __snake_case			:Dict          =						torch.Generator(device=a__    ).manual_seed(a__    )
            __snake_case			:Union[str, Any]          =						{
                """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 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						"""cpu"""

            __snake_case			:Dict          =						self.get_dummy_components()
            __snake_case			:List[str]          =						self.pipeline_class(**a__    )
            pipe.to(a__    )
            pipe.set_progress_bar_config(disable=a__    )

            __snake_case			:Optional[int]          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[Any]          =						pipe(**a__    ).images
            __snake_case			:List[Any]          =						image[0, -3:, -3:, -1]

            self.assertEqual(image.shape			,						(1, 2_56, 2_56, 3)    )
            __snake_case			:Dict          =						np.array(
                [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]    )
            __snake_case			:int          =						np.abs(image_slice.flatten() - expected_slice    ).max()
            self.assertLessEqual(a__			,						1e-3    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            super().test_attention_slicing_forward_pass(expected_max_diff=7e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_cpu_offload_forward_pass(expected_max_diff=3e-3    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_inference_batch_single_identical(expected_max_diff=7e-3    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3    )



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






            '''simple docstring'''





            super().test_save_load_local(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            super().test_save_load_optional_components(expected_max_difference=3e-3    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Dict          =						[
                """DDIMScheduler""",
                """DDPMScheduler""",
                """PNDMScheduler""",
                """HeunDiscreteScheduler""",
                """EulerAncestralDiscreteScheduler""",
                """KDPM2DiscreteScheduler""",
                """KDPM2AncestralDiscreteScheduler""",
                """DPMSolverSDEScheduler""",
            ]
            __snake_case			:List[str]          =						self.get_dummy_components()
            __snake_case			:Optional[Any]          =						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__    )
            __snake_case			:Dict          =						self.get_dummy_inputs(a__    )
            __snake_case			:List[str]          =						2

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

                 __snake_case			:List[str]          =						getattr(a__			,						scheduler_enum.name    )
                 __snake_case			:Union[str, Any]          =						scheduler_cls.from_config(pipe.scheduler.config    )
                 __snake_case			:Union[str, Any]          =						pipe(**a__    )[0]
                 outputs.append(a__    )

            assert check_same_shape(a__    )




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

       '''simple docstring'''



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






            '''simple docstring'''





            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()



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






            '''simple docstring'''





            __snake_case			:int          =						torch.manual_seed(33    )

            __snake_case			:Optional[int]          =						StableDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""			,						torch_dtype=torch.floataa    )
            pipe.to("""cuda"""    )

            __snake_case			:Optional[int]          =						StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""			,						torch_dtype=torch.floataa    )
            upscaler.to("""cuda"""    )

            __snake_case			:Dict          =						"""a photo of an astronaut high resolution, unreal engine, ultra realistic"""

            __snake_case			:Any          =						pipe(a__			,						generator=a__			,						output_type="""latent"""    ).images

            __snake_case			:Any          =						upscaler(
                prompt=a__			,						image=a__			,						num_inference_steps=20			,						guidance_scale=0			,						generator=a__			,						output_type="""np"""			,						).images[0]

            __snake_case			:Dict          =						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 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						torch.manual_seed(33    )

            __snake_case			:Any          =						StableDiffusionLatentUpscalePipeline.from_pretrained(
                """stabilityai/sd-x2-latent-upscaler"""			,						torch_dtype=torch.floataa    )
            upscaler.to("""cuda"""    )

            __snake_case			:Dict          =						"""the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"""

            __snake_case			:List[str]          =						load_image(
                """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"""    )

            __snake_case			:Any          =						upscaler(
                prompt=a__			,						image=a__			,						num_inference_steps=20			,						guidance_scale=0			,						generator=a__			,						output_type="""np"""			,						).images[0]

            __snake_case			:Optional[Any]          =						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










import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402


lowerCamelCase__							=     get_tests_dir("""fixtures/dummy-config.json""")




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						0



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            self.assertIsNotNone(transformers.models.auto.__spec__    )
            self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto"""    )    )



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






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained("""bert-base-uncased"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						AutoConfig.for_model("""roberta"""    )
            self.assertIsInstance(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            with tempfile.TemporaryDirectory() as tmp_dir:
                 # This model name contains bert and roberta, but roberta ends up being picked.
                 __snake_case			:List[Any]          =						os.path.join(a__			,						"""fake-roberta"""    )
                 os.makedirs(a__			,						exist_ok=a__    )
                 with open(os.path.join(a__			,						"""config.json"""    )			,						"""w"""    ) as f:
                      f.write(json.dumps({}    )    )
                 __snake_case			:Optional[Any]          =						AutoConfig.from_pretrained(a__    )
                 self.assertEqual(type(a__    )			,						a__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            try:
                 AutoConfig.register("""custom"""			,						a__    )
                 # Wrong model type will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""model"""			,						a__    )
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(a__    ):
                      AutoConfig.register("""bert"""			,						a__    )

                 # Now that the config is registered, it can be used as any other config with the auto-API
                 __snake_case			:Optional[int]          =						CustomConfig()
                 with tempfile.TemporaryDirectory() as tmp_dir:
                      config.save_pretrained(a__    )
                      __snake_case			:Tuple          =						AutoConfig.from_pretrained(a__    )
                      self.assertIsInstance(a__			,						a__    )

            finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["custom"]



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""bert-base is not a local folder and is not a valid model identifier"""    ):
                 __snake_case			:str          =						AutoConfig.from_pretrained("""bert-base"""    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained(a__			,						revision="""aaaaaa"""    )



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






            '''simple docstring'''





            with self.assertRaisesRegex(
                a__			,						"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json."""			,						):
                 __snake_case			:Optional[int]          =						AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo"""    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            with self.assertRaises(a__    ):
                 __snake_case			:Dict          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(a__    ):
                 __snake_case			:int          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )

            __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
            self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            # Test config can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                 config.save_pretrained(a__    )
                 __snake_case			:Union[str, Any]          =						AutoConfig.from_pretrained(a__			,						trust_remote_code=a__    )
            self.assertEqual(reloaded_config.__class__.__name__			,						"""NewModelConfig"""    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            class 			snake_case__   (							lowercase_):

                   '''simple docstring'''
                   lowerCamelCase							:				Optional[Any] 				= "new-model"

            try:
                 AutoConfig.register("""new-model"""			,						a__    )
                 # If remote code is not set, the default is to use local
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote code is disabled, we load the local one.
                 __snake_case			:Tuple          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfigLocal"""    )

                 # If remote is enabled, we load from the Hub
                 __snake_case			:List[Any]          =						AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model"""			,						trust_remote_code=a__    )
                 self.assertEqual(config.__class__.__name__			,						"""NewModelConfig"""    )

            finally:
                 if "new-model" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["new-model"]


