
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     (      snake_case_			,       snake_case_			,       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     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					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-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  pad_token_id=1						,  vocab_size=1000						,  )
					lowerCamelCase_						=			CLIPTextModel(lowercase )
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase=0 )				->       List[str]:
					lowerCamelCase_						=			torch.manual_seed(lowercase )
					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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline(**lowercase )
					lowerCamelCase_						=			sd_pipe.to(lowercase )
					sd_pipe.set_progress_bar_config(disable=lowercase )

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       int:
					super().test_inference_batch_consistent(batch_sizes=[1, 2] )



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



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

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

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline(**lowercase )
					lowerCamelCase_						=			sd_pipe.to(lowercase )
					sd_pipe.set_progress_bar_config(disable=lowercase )

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

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			EulerAncestralDiscreteScheduler(
					    beta_start=0.0_0_0_8_5						,  beta_end=0.0_1_2						,  beta_schedule="scaled_linear" )
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline(**lowercase )
					lowerCamelCase_						=			sd_pipe.to(lowercase )
					sd_pipe.set_progress_bar_config(disable=lowercase )

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

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					lowerCamelCase_						=			"cpu"  # ensure determinism for the device-dependent torch.Generator
					lowerCamelCase_						=			self.get_dummy_components()
					lowerCamelCase_						=			PNDMScheduler(
					    beta_start=0.0_0_0_8_5						,  beta_end=0.0_1_2						,  beta_schedule="scaled_linear"						,  skip_prk_steps=lowercase )
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline(**lowercase )
					lowerCamelCase_						=			sd_pipe.to(lowercase )
					sd_pipe.set_progress_bar_config(disable=lowercase )

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

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

					lowerCamelCase_						=			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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase=0 )				->       Any:
					lowerCamelCase_						=			torch.manual_seed(lowercase )
					lowerCamelCase_						=			{
					    "prompt": "a photo of the dolomites",
					    "generator": generator,
					    "num_inference_steps": 3,
					    "guidance_scale": 7.5,
					    "output_type": "numpy",
					}
					return inputs



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					lowerCamelCase_						=			"stabilityai/stable-diffusion-2-base"
					lowerCamelCase_						=			DDIMScheduler.from_pretrained(lowercase						,  subfolder="scheduler" )
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline.from_pretrained(lowercase						,  scheduler=lowercase						,  safety_checker=lowercase )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )
					pipe.enable_attention_slicing()

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

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

					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline.from_pretrained(
					    "stabilityai/stable-diffusion-2-base"						,  safety_checker=lowercase )
					lowerCamelCase_						=			LMSDiscreteScheduler.from_config(pipe.scheduler.config )
					pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )
					pipe.enable_attention_slicing()

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

					assert image.shape == (1, 512, 2048, 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     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			0

					def callback_fn(lowercase						,  lowercase						,  lowercase ) -> 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(
													    [
													        0.1_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:
													lowerCamelCase_						=			latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 256)
													lowerCamelCase_						=			latents[0, -3:, -3:, -1]

													lowerCamelCase_						=			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

					lowerCamelCase_						=			False

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

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



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					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(lowercase						,  subfolder="scheduler" )
					lowerCamelCase_						=			StableDiffusionPanoramaPipeline.from_pretrained(lowercase						,  scheduler=lowercase						,  safety_checker=lowercase )
					lowerCamelCase_						=			pipe.to(lowercase )
					pipe.set_progress_bar_config(disable=lowercase )
					pipe.enable_attention_slicing(1 )
					pipe.enable_sequential_cpu_offload()

					lowerCamelCase_						=			self.get_inputs()
					lowerCamelCase_						=			pipe(**lowercase )

					lowerCamelCase_						=			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 TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Union[str, Any]        =      {
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""deberta-v2"""






     def __init__(       self  :  Union[str, Any], _snake_case  :  Dict=1_2_8_1_0_0, _snake_case  :  Any=1_5_3_6, _snake_case  :  Tuple=2_4, _snake_case  :  int=2_4, _snake_case  :  Optional[int]=6_1_4_4, _snake_case  :  Optional[int]="gelu", _snake_case  :  Optional[int]=0.1, _snake_case  :  List[str]=0.1, _snake_case  :  str=5_1_2, _snake_case  :  Optional[int]=0, _snake_case  :  Optional[int]=0.0_2, _snake_case  :  Dict=1e-7, _snake_case  :  int=False, _snake_case  :  Any=-1, _snake_case  :  List[str]=0, _snake_case  :  Tuple=True, _snake_case  :  Any=None, _snake_case  :  Union[str, Any]=0, _snake_case  :  Tuple="gelu", **_snake_case  :  Union[str, Any], )   ->Optional[int]:
            super().__init__(**_snake_case					)

            snake_case__							:	Dict   	=	hidden_size
            snake_case__							:	Optional[int]   	=	num_hidden_layers
            snake_case__							:	Any   	=	num_attention_heads
            snake_case__							:	List[Any]   	=	intermediate_size
            snake_case__							:	List[Any]   	=	hidden_act
            snake_case__							:	Union[str, Any]   	=	hidden_dropout_prob
            snake_case__							:	Dict   	=	attention_probs_dropout_prob
            snake_case__							:	List[str]   	=	max_position_embeddings
            snake_case__							:	List[str]   	=	type_vocab_size
            snake_case__							:	Optional[Any]   	=	initializer_range
            snake_case__							:	Optional[int]   	=	relative_attention
            snake_case__							:	Tuple   	=	max_relative_positions
            snake_case__							:	Union[str, Any]   	=	pad_token_id
            snake_case__							:	Optional[int]   	=	position_biased_input

            # Backwards compatibility
            if type(_snake_case					) == str:
                   snake_case__							:	int   	=	[x.strip() for x in pos_att_type.lower().split('|'					)]

            snake_case__							:	List[str]   	=	pos_att_type
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	Optional[int]   	=	layer_norm_eps

            snake_case__							:	Optional[int]   	=	kwargs.get('pooler_hidden_size', _snake_case					)
            snake_case__							:	int   	=	pooler_dropout
            snake_case__							:	str   	=	pooler_hidden_act




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     @property
     def    lowercase_   (       self  :  Optional[int]					)   ->Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                   snake_case__							:	List[Any]   	=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   snake_case__							:	int   	=	{0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]					)
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->int:
            return 1_2






     def    lowercase_   (       self  :  Tuple, _snake_case  :  Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"], _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  int = -1, _snake_case  :  bool = False, _snake_case  :  Optional["TensorType"] = None, _snake_case  :  int = 3, _snake_case  :  int = 4_0, _snake_case  :  int = 4_0, _snake_case  :  "PreTrainedTokenizerBase" = None, )   ->Mapping[str, Any]:
            snake_case__							:	Union[str, Any]   	=	super().generate_dummy_inputs(preprocessor=_snake_case, framework=_snake_case					)
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs






