
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

lowerCAmelCase					:    Tuple           = logging.get_logger(__name__)

lowerCAmelCase					:    int           = {
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""imagegpt"""
				lowerCAmelCase_     			=							["""past_key_values"""]
				lowerCAmelCase_     			=							{
				    """hidden_size""": """n_embd""",
				    """max_position_embeddings""": """n_positions""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}







				def __init__(       self						,				A_=512 + 1						,				A_=32 * 32						,				A_=512						,				A_=24						,				A_=8						,				A_=None						,				A_="quick_gelu"						,				A_=0.1						,				A_=0.1						,				A_=0.1						,				A_=1e-5						,				A_=0.02						,				A_=True						,				A_=True						,				A_=False						,				A_=False						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     n_positions
											UpperCamelCase   			=     n_embd
											UpperCamelCase   			=     n_layer
											UpperCamelCase   			=     n_head
											UpperCamelCase   			=     n_inner
											UpperCamelCase   			=     activation_function
											UpperCamelCase   			=     resid_pdrop
											UpperCamelCase   			=     embd_pdrop
											UpperCamelCase   			=     attn_pdrop
											UpperCamelCase   			=     layer_norm_epsilon
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     scale_attn_weights
											UpperCamelCase   			=     use_cache
											UpperCamelCase   			=     scale_attn_by_inverse_layer_idx
											UpperCamelCase   			=     reorder_and_upcast_attn
											UpperCamelCase   			=     tie_word_embeddings

											super().__init__(tie_word_embeddings=A_						,				**A_     )


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											    ]     )







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = 1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 32						,				A_ = 32						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
											UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )

											return inputs









"""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       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     StableDiffusionPanoramaPipeline
			__magic_name__     					=     TEXT_TO_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_BATCH_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS
			__magic_name__     					=     TEXT_TO_IMAGE_IMAGE_PARAMS



			def 				__lowercase					(			self							:				Optional[int]		)							->					int:
									torch.manual_seed(0		)
									snake_case   :			Optional[Any]						    = 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   ,	)
									snake_case   :			Tuple						    = DDIMScheduler()
									torch.manual_seed(0		)
									snake_case   :			List[Any]						    = 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		)
									snake_case   :			str						    = 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   ,	)
									snake_case   :			int						    = CLIPTextModel(_lowercase		)
									snake_case   :			Union[str, Any]						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

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



			def 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				int   ,	_lowercase							:				Dict=0		)							->					Optional[int]:
									snake_case   :			Union[str, Any]						    = torch.manual_seed(_lowercase		)
									snake_case   :			str						    = {
									    "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 				__lowercase					(			self							:				str		)							->					List[str]:
									snake_case   :			Any						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			Dict						    = self.get_dummy_components()
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[Any]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Union[str, Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									snake_case   :			List[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 				__lowercase					(			self							:				int		)							->					Union[str, Any]:
									super().test_inference_batch_consistent(batch_sizes=[1, 2]		)



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



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

									snake_case   :			List[str]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			int						    = "french fries"
									snake_case   :			Union[str, Any]						    = sd_pipe(**_lowercase   ,	negative_prompt=_lowercase		)
									snake_case   :			str						    = output.images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1]

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

									snake_case   :			Union[str, Any]						    = 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 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Optional[Any]						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[Any]						    = self.get_dummy_components()
									snake_case   :			List[Any]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			int						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

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

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

									snake_case   :			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 				__lowercase					(			self							:				int		)							->					Optional[Any]:
									snake_case   :			Dict						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			List[str]						    = self.get_dummy_components()
									snake_case   :			Any						    = EulerAncestralDiscreteScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"		)
									snake_case   :			List[str]						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			List[str]						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			List[Any]						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[Any]						    = sd_pipe(**_lowercase		).images
									snake_case   :			int						    = image[0, -3:, -3:, -1]

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

									snake_case   :			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 				__lowercase					(			self							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			str						    = "cpu"  # ensure determinism for the device-dependent torch.Generator
									snake_case   :			str						    = self.get_dummy_components()
									snake_case   :			Optional[int]						    = PNDMScheduler(
									    beta_start=0.00085   ,	beta_end=0.012   ,	beta_schedule="scaled_linear"   ,	skip_prk_steps=_lowercase		)
									snake_case   :			Dict						    = StableDiffusionPanoramaPipeline(**_lowercase		)
									snake_case   :			Dict						    = sd_pipe.to(_lowercase		)
									sd_pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = self.get_dummy_inputs(_lowercase		)
									snake_case   :			Optional[int]						    = sd_pipe(**_lowercase		).images
									snake_case   :			str						    = image[0, -3:, -3:, -1]

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

									snake_case   :			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       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



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



			def 				__lowercase					(			self							:				Optional[Any]		)							->					Union[str, Any]:
									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			Dict						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Any						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

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

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

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

									snake_case   :			Dict						    = self.get_inputs()
									snake_case   :			int						    = pipe(**_lowercase		).images
									snake_case   :			Optional[Any]						    = image[0, -3:, -3:, -1].flatten()

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

									snake_case   :			List[str]						    = 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 				__lowercase					(			self							:				str		)							->					Any:
									snake_case   :			Any						    = 0

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

																					snake_case   :			Tuple						    = 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:
																					snake_case   :			Union[str, Any]						    = latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					snake_case   :			Tuple						    = latents[0, -3:, -3:, -1]

																					snake_case   :			Tuple						    = 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

									snake_case   :			Dict						    = False

									snake_case   :			List[Any]						    = "stabilityai/stable-diffusion-2-base"
									snake_case   :			List[str]						    = DDIMScheduler.from_pretrained(_lowercase   ,	subfolder="scheduler"		)
									snake_case   :			Optional[Any]						    = StableDiffusionPanoramaPipeline.from_pretrained(_lowercase   ,	scheduler=_lowercase   ,	safety_checker=_lowercase		)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)
									pipe.enable_attention_slicing()

									snake_case   :			Tuple						    = self.get_inputs()
									pipe(**_lowercase   ,	callback=_lowercase   ,	callback_steps=1		)
									assert callback_fn.has_been_called
									assert number_of_steps == 3



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									snake_case   :			Any						    = self.get_inputs()
									snake_case   :			Optional[int]						    = pipe(**_lowercase		)

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



