



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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						SCREAMING_SNAKE_CASE__ ,						unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: Tuple					=      StableDiffusionPanoramaPipeline
    __lowerCamelCase			: List[str]					=      TEXT_TO_IMAGE_PARAMS
    __lowerCamelCase			: Any					=      TEXT_TO_IMAGE_BATCH_PARAMS
    __lowerCamelCase			: Union[str, Any]					=      TEXT_TO_IMAGE_IMAGE_PARAMS
    __lowerCamelCase			: Tuple					=      TEXT_TO_IMAGE_IMAGE_PARAMS






    def 			_lowerCAmelCase					(       self		):
        torch.manual_seed(0		)
        A :       str          =   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 :       str          =   DDIMScheduler()
        torch.manual_seed(0		)
        A :       str          =   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 :       Tuple          =   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=1000,		)
        A :       List[Any]          =   CLIPTextModel(lowerCamelCase__		)
        A :       Tuple          =   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

        A :       List[str]          =   {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=0		):
        A :       List[Any]          =   torch.manual_seed(lowerCamelCase__		)
        A :       Tuple          =   {
            """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 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        A :       Any          =   self.get_dummy_components()
        A :       Optional[Any]          =   StableDiffusionPanoramaPipeline(**lowerCamelCase__		)
        A :       Optional[int]          =   sd_pipe.to(lowerCamelCase__		)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       List[Any]          =   self.get_dummy_inputs(lowerCamelCase__		)
        A :       Tuple          =   sd_pipe(**lowerCamelCase__		).images
        A :       Optional[int]          =   image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        A :       Optional[Any]          =   np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]		)

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






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






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






    def 			_lowerCAmelCase					(       self		):
        A :       Tuple          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        A :       Optional[Any]          =   self.get_dummy_components()
        A :       Optional[Any]          =   StableDiffusionPanoramaPipeline(**lowerCamelCase__		)
        A :       List[str]          =   sd_pipe.to(lowerCamelCase__		)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Optional[int]          =   self.get_dummy_inputs(lowerCamelCase__		)
        A :       Any          =   """french fries"""
        A :       int          =   sd_pipe(**lowerCamelCase__,		negative_prompt=lowerCamelCase__		)
        A :       Dict          =   output.images
        A :       str          =   image[0, -3:, -3:, -1]

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

        A :       Tuple          =   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[int]          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        A :       str          =   self.get_dummy_components()
        A :       int          =   StableDiffusionPanoramaPipeline(**lowerCamelCase__		)
        A :       Optional[Any]          =   sd_pipe.to(lowerCamelCase__		)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

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

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

        A :       List[str]          =   np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        A :       List[str]          =   self.get_dummy_components()
        A :       Tuple          =   EulerAncestralDiscreteScheduler(
            beta_start=0.0_0085,		beta_end=0.012,		beta_schedule="""scaled_linear"""		)
        A :       int          =   StableDiffusionPanoramaPipeline(**lowerCamelCase__		)
        A :       List[str]          =   sd_pipe.to(lowerCamelCase__		)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       Tuple          =   self.get_dummy_inputs(lowerCamelCase__		)
        A :       int          =   sd_pipe(**lowerCamelCase__		).images
        A :       Any          =   image[0, -3:, -3:, -1]

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

        A :       Tuple          =   np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       List[Any]          =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
        A :       List[Any]          =   self.get_dummy_components()
        A :       List[str]          =   PNDMScheduler(
            beta_start=0.0_0085,		beta_end=0.012,		beta_schedule="""scaled_linear""",		skip_prk_steps=lowerCamelCase__		)
        A :       Tuple          =   StableDiffusionPanoramaPipeline(**lowerCamelCase__		)
        A :       List[str]          =   sd_pipe.to(lowerCamelCase__		)
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__		)

        A :       List[Any]          =   self.get_dummy_inputs(lowerCamelCase__		)
        A :       int          =   sd_pipe(**lowerCamelCase__		).images
        A :       Union[str, Any]          =   image[0, -3:, -3:, -1]

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

        A :       Optional[int]          =   np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]		)

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






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




    '''simple docstring'''






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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__=0		):
        A :       Union[str, Any]          =   torch.manual_seed(lowerCamelCase__		)
        A :       Optional[Any]          =   {
            """prompt""": """a photo of the dolomites""",
            """generator""": generator,
            """num_inference_steps""": 3,
            """guidance_scale""": 7.5,
            """output_type""": """numpy""",
        }
        return inputs






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   """stabilityai/stable-diffusion-2-base"""
        A :       Dict          =   DDIMScheduler.from_pretrained(lowerCamelCase__,		subfolder="""scheduler"""		)
        A :       int          =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__,		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__		)
        pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing()

        A :       Optional[int]          =   self.get_inputs()
        A :       List[str]          =   pipe(**lowerCamelCase__		).images
        A :       Union[str, Any]          =   image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2048, 3)

        A :       str          =   np.array(
            [
                0.3696_8392,
                0.2702_5372,
                0.3244_6766,
                0.2837_9387,
                0.3636_3274,
                0.3073_3347,
                0.2710_0027,
                0.2705_4125,
                0.2553_6096,
            ]		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       Optional[Any]          =   StableDiffusionPanoramaPipeline.from_pretrained(
            """stabilityai/stable-diffusion-2-base""",		safety_checker=lowerCamelCase__		)
        A :       List[Any]          =   LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
        pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing()

        A :       Optional[Any]          =   self.get_inputs()
        A :       int          =   pipe(**lowerCamelCase__		).images
        A :       List[str]          =   image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2048, 3)

        A :       List[Any]          =   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 			_lowerCAmelCase					(       self		):
        A :       Dict          =   0

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

                A :       str          =   np.array(
                    [
                        0.1868_1869,
                        0.3390_7816,
                        0.536_1276,
                        0.1443_2865,
                        -0.0285_6611,
                        -0.7394_1123,
                        0.2339_7987,
                        0.4732_2682,
                        -0.3782_3164,
                    ]		)
                assert np.abs(latents_slice.flatten() - expected_slice		).max() < 5e-2
            elif step == 2:
                A :       Any          =   latents.detach().cpu().numpy()
                assert latents.shape == (1, 4, 64, 256)
                A :       List[Any]          =   latents[0, -3:, -3:, -1]

                A :       int          =   np.array(
                    [
                        0.1853_9645,
                        0.3398_7248,
                        0.537_8559,
                        0.1443_7142,
                        -0.0245_5261,
                        -0.733_8317,
                        0.2399_0755,
                        0.4735_6272,
                        -0.378_6505,
                    ]		)

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

        A :       List[str]          =   False

        A :       int          =   """stabilityai/stable-diffusion-2-base"""
        A :       Dict          =   DDIMScheduler.from_pretrained(lowerCamelCase__,		subfolder="""scheduler"""		)
        A :       int          =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__,		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__		)
        A :       Dict          =   pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing()

        A :       Union[str, Any]          =   self.get_inputs()
        pipe(**lowerCamelCase__,		callback=lowerCamelCase__,		callback_steps=1		)
        assert callback_fn.has_been_called
        assert number_of_steps == 3






    def 			_lowerCAmelCase					(       self		):
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        A :       List[str]          =   """stabilityai/stable-diffusion-2-base"""
        A :       Optional[int]          =   DDIMScheduler.from_pretrained(lowerCamelCase__,		subfolder="""scheduler"""		)
        A :       Tuple          =   StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__,		scheduler=lowerCamelCase__,		safety_checker=lowerCamelCase__		)
        A :       List[Any]          =   pipe.to(lowerCamelCase__		)
        pipe.set_progress_bar_config(disable=lowerCamelCase__		)
        pipe.enable_attention_slicing(1		)
        pipe.enable_sequential_cpu_offload()

        A :       Optional[Any]          =   self.get_inputs()
        A :       Union[str, Any]          =   pipe(**lowerCamelCase__		)

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


