
"""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__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					StableDiffusionPanoramaPipeline
     __a						=					TEXT_TO_IMAGE_PARAMS
     __a						=					TEXT_TO_IMAGE_BATCH_PARAMS
     __a						=					TEXT_TO_IMAGE_IMAGE_PARAMS
     __a						=					TEXT_TO_IMAGE_IMAGE_PARAMS







     def      lowercase	(							self			:			int			):
       torch.manual_seed(0			)
       _snake_case					     =       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					     =       DDIMScheduler()
       torch.manual_seed(0			)
       _snake_case					     =       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					     =       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					     =       CLIPTextModel(_lowerCamelCase			)
       _snake_case					     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

       _snake_case					     =       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Optional[int]    ,      _lowerCamelCase			:			Any=0			):
       _snake_case					     =       torch.manual_seed(_lowerCamelCase			)
       _snake_case					     =       {
           '''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			:			Tuple			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionPanoramaPipeline(**_lowerCamelCase			)
       _snake_case					     =       sd_pipe.to(_lowerCamelCase			)
       sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

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

       _snake_case					     =       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      lowercase	(							self			:			Optional[Any]			):
       super().test_inference_batch_consistent(batch_sizes=[1, 2]			)







     def      lowercase	(							self			:			Union[str, Any]			):
       super().test_inference_batch_single_identical(batch_size=2    ,      expected_max_diff=3.25e-3			)







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

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

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

       _snake_case					     =       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      lowercase	(							self			:			Tuple			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionPanoramaPipeline(**_lowerCamelCase			)
       _snake_case					     =       sd_pipe.to(_lowerCamelCase			)
       sd_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

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

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

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

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

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

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

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

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

       _snake_case					     =       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      lowercase	(							self			:			int			):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[Any]=0			):
       _snake_case					     =       torch.manual_seed(_lowerCamelCase			)
       _snake_case					     =       {
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs







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

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

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

       _snake_case					     =       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      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''    ,      safety_checker=_lowerCamelCase			)
       _snake_case					     =       LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
       pipe.to(_lowerCamelCase			)
       pipe.set_progress_bar_config(disable=_lowerCamelCase			)
       pipe.enable_attention_slicing()

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

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

       _snake_case					     =       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			:			Dict			):
       _snake_case					     =       0

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

           _snake_case					     =       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:
           _snake_case					     =       latents.detach().cpu().numpy()
           assert latents.shape == (1, 4, 64, 256)
           _snake_case					     =       latents[0, -3:, -3:, -1]

           _snake_case					     =       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

       _snake_case					     =       False

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

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







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

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

       _snake_case					     =       self.get_inputs()
       _snake_case					     =       pipe(**_lowerCamelCase			)

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





"""simple docstring"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

UpperCAmelCase__          = ort.SessionOptions()
UpperCAmelCase__          = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__          = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__          = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__          = ort.RunOptions()

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))



