


"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()



@skip_mps
class 			lowerCamelCase__						(   UpperCAmelCase__							,    UpperCAmelCase__							,    unittest.TestCase			):
      lowerCAmelCase       =     StableDiffusionPanoramaPipeline
      lowerCAmelCase       =     TEXT_TO_IMAGE_PARAMS
      lowerCAmelCase       =     TEXT_TO_IMAGE_BATCH_PARAMS
      lowerCAmelCase       =     TEXT_TO_IMAGE_IMAGE_PARAMS
      lowerCAmelCase       =     TEXT_TO_IMAGE_IMAGE_PARAMS







      def __a						(							self   :    Tuple		):
        torch.manual_seed(0		)
        A					       = UNetaDConditionModel(
            block_out_channels=(32, 64)			,			layers_per_block=1			,			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()
        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=1_000			,			)
        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 __a						(							self   :    Any			,			_lowercase   :    Optional[int]			,			_lowercase   :    Dict=0		):
        A					       = torch.manual_seed(__lowerCAmelCase		)
        A					       = {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs







      def __a						(							self   :    Optional[int]		):
        A					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator
        A					       = self.get_dummy_components()
        A					       = StableDiffusionPanoramaPipeline(**__lowerCAmelCase		)
        A					       = sd_pipe.to(__lowerCAmelCase		)
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

        A					       = self.get_dummy_inputs(__lowerCAmelCase		)
        A					       = sd_pipe(**__lowerCAmelCase		).images
        A					       = image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        A					       = np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]		)

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







      def __a						(							self   :    str		):
        super().test_inference_batch_consistent(batch_sizes=[1, 2]		)







      def __a						(							self   :    Any		):
        super().test_inference_batch_single_identical(batch_size=2			,			expected_max_diff=3.25e-3		)







      def __a						(							self   :    Optional[Any]		):
        A					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator
        A					       = self.get_dummy_components()
        A					       = StableDiffusionPanoramaPipeline(**__lowerCAmelCase		)
        A					       = sd_pipe.to(__lowerCAmelCase		)
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

        A					       = self.get_dummy_inputs(__lowerCAmelCase		)
        A					       = 'french fries'
        A					       = sd_pipe(**__lowerCAmelCase			,			negative_prompt=__lowerCAmelCase		)
        A					       = output.images
        A					       = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A					       = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]		)

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







      def __a						(							self   :    List[str]		):
        A					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator
        A					       = self.get_dummy_components()
        A					       = StableDiffusionPanoramaPipeline(**__lowerCAmelCase		)
        A					       = sd_pipe.to(__lowerCAmelCase		)
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

        A					       = self.get_dummy_inputs(__lowerCAmelCase		)
        A					       = sd_pipe(**__lowerCAmelCase			,			view_batch_size=2		)
        A					       = output.images
        A					       = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A					       = np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]		)

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







      def __a						(							self   :    str		):
        A					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator
        A					       = self.get_dummy_components()
        A					       = EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			beta_schedule='scaled_linear'		)
        A					       = StableDiffusionPanoramaPipeline(**__lowerCAmelCase		)
        A					       = sd_pipe.to(__lowerCAmelCase		)
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

        A					       = self.get_dummy_inputs(__lowerCAmelCase		)
        A					       = sd_pipe(**__lowerCAmelCase		).images
        A					       = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A					       = np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]		)

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







      def __a						(							self   :    List[str]		):
        A					       = 'cpu'  # ensure determinism for the device-dependent torch.Generator
        A					       = self.get_dummy_components()
        A					       = PNDMScheduler(
            beta_start=0.0_0_0_8_5			,			beta_end=0.0_1_2			,			beta_schedule='scaled_linear'			,			skip_prk_steps=__lowerCAmelCase		)
        A					       = StableDiffusionPanoramaPipeline(**__lowerCAmelCase		)
        A					       = sd_pipe.to(__lowerCAmelCase		)
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase		)

        A					       = self.get_dummy_inputs(__lowerCAmelCase		)
        A					       = sd_pipe(**__lowerCAmelCase		).images
        A					       = image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        A					       = np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]		)

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



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







      def __a						(							self   :    List[str]		):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







      def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[str]=0		):
        A					       = torch.manual_seed(__lowerCAmelCase		)
        A					       = {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs







      def __a						(							self   :    Tuple		):
        A					       = 'stabilityai/stable-diffusion-2-base'
        A					       = DDIMScheduler.from_pretrained(__lowerCAmelCase			,			subfolder='scheduler'		)
        A					       = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,			scheduler=__lowerCAmelCase			,			safety_checker=__lowerCAmelCase		)
        pipe.to(__lowerCAmelCase		)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
        pipe.enable_attention_slicing()

        A					       = self.get_inputs()
        A					       = pipe(**__lowerCAmelCase		).images
        A					       = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2_048, 3)

        A					       = np.array(
            [
                0.3_6_9_6_8_3_9_2,
                0.2_7_0_2_5_3_7_2,
                0.3_2_4_4_6_7_6_6,
                0.2_8_3_7_9_3_8_7,
                0.3_6_3_6_3_2_7_4,
                0.3_0_7_3_3_3_4_7,
                0.2_7_1_0_0_0_2_7,
                0.2_7_0_5_4_1_2_5,
                0.2_5_5_3_6_0_9_6,
            ]		)

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







      def __a						(							self   :    Optional[int]		):
        A					       = StableDiffusionPanoramaPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-base'			,			safety_checker=__lowerCAmelCase		)
        A					       = LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
        pipe.to(__lowerCAmelCase		)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
        pipe.enable_attention_slicing()

        A					       = self.get_inputs()
        A					       = pipe(**__lowerCAmelCase		).images
        A					       = image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2_048, 3)

        A					       = np.array(
            [
                [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ]
            ]		)

        assert np.abs(expected_slice - image_slice		).max() < 1e-3







      def __a						(							self   :    Dict		):
        A					       = 0

        def callback_fn(_lowercase   :    int			,			_lowercase   :    int			,			_lowercase   :    torch.FloatTensor		) -> None:
          A					       = True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 1:
            A					       = latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 256)
            A					       = latents[0, -3:, -3:, -1]

            A					       = np.array(
                [
                    0.1_8_6_8_1_8_6_9,
                    0.3_3_9_0_7_8_1_6,
                    0.5_3_6_1_2_7_6,
                    0.1_4_4_3_2_8_6_5,
                    -0.0_2_8_5_6_6_1_1,
                    -0.7_3_9_4_1_1_2_3,
                    0.2_3_3_9_7_9_8_7,
                    0.4_7_3_2_2_6_8_2,
                    -0.3_7_8_2_3_1_6_4,
                ]		)
            assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
          elif step == 2:
            A					       = latents.detach().cpu().numpy()
            assert latents.shape == (1, 4, 64, 256)
            A					       = latents[0, -3:, -3:, -1]

            A					       = np.array(
                [
                    0.1_8_5_3_9_6_4_5,
                    0.3_3_9_8_7_2_4_8,
                    0.5_3_7_8_5_5_9,
                    0.1_4_4_3_7_1_4_2,
                    -0.0_2_4_5_5_2_6_1,
                    -0.7_3_3_8_3_1_7,
                    0.2_3_9_9_0_7_5_5,
                    0.4_7_3_5_6_2_7_2,
                    -0.3_7_8_6_5_0_5,
                ]		)

            assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2

        A					       = False

        A					       = 'stabilityai/stable-diffusion-2-base'
        A					       = DDIMScheduler.from_pretrained(__lowerCAmelCase			,			subfolder='scheduler'		)
        A					       = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,			scheduler=__lowerCAmelCase			,			safety_checker=__lowerCAmelCase		)
        A					       = pipe.to(__lowerCAmelCase		)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
        pipe.enable_attention_slicing()

        A					       = self.get_inputs()
        pipe(**__lowerCAmelCase			,			callback=__lowerCAmelCase			,			callback_steps=1		)
        assert callback_fn.has_been_called
        assert number_of_steps == 3







      def __a						(							self   :    List[str]		):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A					       = 'stabilityai/stable-diffusion-2-base'
        A					       = DDIMScheduler.from_pretrained(__lowerCAmelCase			,			subfolder='scheduler'		)
        A					       = StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,			scheduler=__lowerCAmelCase			,			safety_checker=__lowerCAmelCase		)
        A					       = pipe.to(__lowerCAmelCase		)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase		)
        pipe.enable_attention_slicing(1		)
        pipe.enable_sequential_cpu_offload()

        A					       = self.get_inputs()
        A					       = pipe(**__lowerCAmelCase		)

        A					       = torch.cuda.max_memory_allocated()
        # make sure that less than 5.2 GB is allocated
        assert mem_bytes < 5.5 * 10**9









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





