
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					(	__a					,      __a					,      unittest.TestCase       ):
				_lowercase					=StableDiffusionPanoramaPipeline
				_lowercase					=TEXT_TO_IMAGE_PARAMS
				_lowercase					=TEXT_TO_IMAGE_BATCH_PARAMS
				_lowercase					=TEXT_TO_IMAGE_IMAGE_PARAMS
				_lowercase					=TEXT_TO_IMAGE_IMAGE_PARAMS




				def      __a						(					self     )				->      int:
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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				,				)
							lowerCAmelCase_						=	DDIMScheduler()
							torch.manual_seed(0     )
							lowerCAmelCase_						=	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     )
							lowerCAmelCase_						=	CLIPTextConfig(
							    bos_token_id=0				,				eos_token_id=2				,				hidden_size=32				,				intermediate_size=37				,				layer_norm_eps=1e-0_5				,				num_attention_heads=4				,				num_hidden_layers=5				,				pad_token_id=1				,				vocab_size=1_000				,				)
							lowerCAmelCase_						=	CLIPTextModel(_UpperCamelCase     )
							lowerCAmelCase_						=	CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=0     )				->      Union[str, Any]:
							lowerCAmelCase_						=	torch.manual_seed(_UpperCamelCase     )
							lowerCAmelCase_						=	{
							    "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[Any]:
							lowerCAmelCase_						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline(**_UpperCamelCase     )
							lowerCAmelCase_						=	sd_pipe.to(_UpperCamelCase     )
							sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

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

							lowerCAmelCase_						=	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      __a						(					self     )				->      Any:
							super().test_inference_batch_consistent(batch_sizes=[1, 2]     )




				def      __a						(					self     )				->      str:
							super().test_inference_batch_single_identical(batch_size=2				,				expected_max_diff=3.2_5e-3     )




				def      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline(**_UpperCamelCase     )
							lowerCAmelCase_						=	sd_pipe.to(_UpperCamelCase     )
							sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

							lowerCAmelCase_						=	self.get_dummy_inputs(_UpperCamelCase     )
							lowerCAmelCase_						=	"french fries"
							lowerCAmelCase_						=	sd_pipe(**_UpperCamelCase				,				negative_prompt=_UpperCamelCase     )
							lowerCAmelCase_						=	output.images
							lowerCAmelCase_						=	image[0, -3:, -3:, -1]

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

							lowerCAmelCase_						=	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      __a						(					self     )				->      Union[str, Any]:
							lowerCAmelCase_						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline(**_UpperCamelCase     )
							lowerCAmelCase_						=	sd_pipe.to(_UpperCamelCase     )
							sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

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

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

							lowerCAmelCase_						=	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      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	EulerAncestralDiscreteScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"     )
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline(**_UpperCamelCase     )
							lowerCAmelCase_						=	sd_pipe.to(_UpperCamelCase     )
							sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

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

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

							lowerCAmelCase_						=	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      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	"cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase_						=	self.get_dummy_components()
							lowerCAmelCase_						=	PNDMScheduler(
							    beta_start=0.00085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				skip_prk_steps=_UpperCamelCase     )
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline(**_UpperCamelCase     )
							lowerCAmelCase_						=	sd_pipe.to(_UpperCamelCase     )
							sd_pipe.set_progress_bar_config(disable=_UpperCamelCase     )

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

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

							lowerCAmelCase_						=	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 	_lowerCAmelCase					(	unittest.TestCase       ):




				def      __a						(					self     )				->      int:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def      __a						(					self				,				_UpperCamelCase=0     )				->      Optional[Any]:
							lowerCAmelCase_						=	torch.manual_seed(_UpperCamelCase     )
							lowerCAmelCase_						=	{
							    "prompt": "a photo of the dolomites",
							    "generator": generator,
							    "num_inference_steps": 3,
							    "guidance_scale": 7.5,
							    "output_type": "numpy",
							}
							return inputs




				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"stabilityai/stable-diffusion-2-base"
							lowerCAmelCase_						=	DDIMScheduler.from_pretrained(_UpperCamelCase				,				subfolder="scheduler"     )
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline.from_pretrained(_UpperCamelCase				,				scheduler=_UpperCamelCase				,				safety_checker=_UpperCamelCase     )
							pipe.to(_UpperCamelCase     )
							pipe.set_progress_bar_config(disable=_UpperCamelCase     )
							pipe.enable_attention_slicing()

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

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

							lowerCAmelCase_						=	np.array(
							    [
							        0.36968392,
							        0.27025372,
							        0.32446766,
							        0.28379387,
							        0.36363274,
							        0.30733347,
							        0.27100027,
							        0.27054125,
							        0.25536096,
							    ]     )

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




				def      __a						(					self     )				->      Dict:
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline.from_pretrained(
							    "stabilityai/stable-diffusion-2-base"				,				safety_checker=_UpperCamelCase     )
							lowerCAmelCase_						=	LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
							pipe.to(_UpperCamelCase     )
							pipe.set_progress_bar_config(disable=_UpperCamelCase     )
							pipe.enable_attention_slicing()

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

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

							lowerCAmelCase_						=	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:
							lowerCAmelCase_						=	0

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

													lowerCAmelCase_						=	np.array(
													    [
													        0.18681869,
													        0.33907816,
													        0.5361276,
													        0.14432865,
													        -0.02856611,
													        -0.73941123,
													        0.23397987,
													        0.47322682,
													        -0.37823164,
													    ]     )
													assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
										elif step == 2:
													lowerCAmelCase_						=	latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 256)
													lowerCAmelCase_						=	latents[0, -3:, -3:, -1]

													lowerCAmelCase_						=	np.array(
													    [
													        0.18539645,
													        0.33987248,
													        0.5378559,
													        0.14437142,
													        -0.02455261,
													        -0.7338317,
													        0.23990755,
													        0.47356272,
													        -0.3786505,
													    ]     )

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

							lowerCAmelCase_						=	False

							lowerCAmelCase_						=	"stabilityai/stable-diffusion-2-base"
							lowerCAmelCase_						=	DDIMScheduler.from_pretrained(_UpperCamelCase				,				subfolder="scheduler"     )
							lowerCAmelCase_						=	StableDiffusionPanoramaPipeline.from_pretrained(_UpperCamelCase				,				scheduler=_UpperCamelCase				,				safety_checker=_UpperCamelCase     )
							lowerCAmelCase_						=	pipe.to(_UpperCamelCase     )
							pipe.set_progress_bar_config(disable=_UpperCamelCase     )
							pipe.enable_attention_slicing()

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




				def      __a						(					self     )				->      Union[str, Any]:
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

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

							lowerCAmelCase_						=	self.get_inputs()
							lowerCAmelCase_						=	pipe(**_UpperCamelCase     )

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


_A   =     {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



