





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       (      lowerCamelCase__ ,lowerCamelCase__ ,unittest.TestCase  ):




      """simple docstring"""







      UpperCAmelCase__						=      StableDiffusionPanoramaPipeline
      UpperCAmelCase__						=      TEXT_TO_IMAGE_PARAMS
      UpperCAmelCase__						=      TEXT_TO_IMAGE_BATCH_PARAMS
      UpperCAmelCase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS
      UpperCAmelCase__						=      TEXT_TO_IMAGE_IMAGE_PARAMS


      def 				__snake_case			(		self):



             """simple docstring"""





             torch.manual_seed(0)
             _lowerCamelCase							:     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      ,							)
             _lowerCamelCase							:     Dict	=		DDIMScheduler()
             torch.manual_seed(0)
             _lowerCamelCase							:     List[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)
             _lowerCamelCase							:     Dict	=		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							:     List[Any]	=		CLIPTextModel(a__)
             _lowerCamelCase							:     Dict	=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

             _lowerCamelCase							:     Optional[int]	=		{
                 '''unet''': unet,
                 '''scheduler''': scheduler,
                 '''vae''': vae,
                 '''text_encoder''': text_encoder,
                 '''tokenizer''': tokenizer,
                 '''safety_checker''': None,
                 '''feature_extractor''': None,
             }
             return components


      def 				__snake_case			(		self      ,							a__      ,							a__=0):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		torch.manual_seed(a__)
             _lowerCamelCase							:     int	=		{
                 '''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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase							:     Any	=		self.get_dummy_components()
             _lowerCamelCase							:     List[str]	=		StableDiffusionPanoramaPipeline(**a__)
             _lowerCamelCase							:     int	=		sd_pipe.to(a__)
             sd_pipe.set_progress_bar_config(disable=a__)

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

             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             super().test_inference_batch_consistent(batch_sizes=[1, 2])


      def 				__snake_case			(		self):



             """simple docstring"""





             super().test_inference_batch_single_identical(batch_size=2      ,							expected_max_diff=3.25e-3)


      def 				__snake_case			(		self):



             """simple docstring"""





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

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

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

             _lowerCamelCase							:     Optional[int]	=		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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase							:     List[str]	=		self.get_dummy_components()
             _lowerCamelCase							:     List[str]	=		StableDiffusionPanoramaPipeline(**a__)
             _lowerCamelCase							:     Union[str, Any]	=		sd_pipe.to(a__)
             sd_pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase							:     List[str]	=		self.get_dummy_inputs(a__)
             _lowerCamelCase							:     int	=		sd_pipe(**a__      ,							view_batch_size=2)
             _lowerCamelCase							:     Union[str, Any]	=		output.images
             _lowerCamelCase							:     Tuple	=		image[0, -3:, -3:, -1]

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

             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
             _lowerCamelCase							:     Union[str, Any]	=		self.get_dummy_components()
             _lowerCamelCase							:     str	=		EulerAncestralDiscreteScheduler(
                 beta_start=0.0_0085      ,							beta_end=0.012      ,							beta_schedule='''scaled_linear''')
             _lowerCamelCase							:     str	=		StableDiffusionPanoramaPipeline(**a__)
             _lowerCamelCase							:     Any	=		sd_pipe.to(a__)
             sd_pipe.set_progress_bar_config(disable=a__)

             _lowerCamelCase							:     Tuple	=		self.get_dummy_inputs(a__)
             _lowerCamelCase							:     Dict	=		sd_pipe(**a__).images
             _lowerCamelCase							:     List[str]	=		image[0, -3:, -3:, -1]

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

             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





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

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

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

             _lowerCamelCase							:     Any	=		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  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 				__snake_case			(		self      ,							a__=0):



             """simple docstring"""





             _lowerCamelCase							:     int	=		torch.manual_seed(a__)
             _lowerCamelCase							:     Optional[int]	=		{
                 '''prompt''': '''a photo of the dolomites''',
                 '''generator''': generator,
                 '''num_inference_steps''': 3,
                 '''guidance_scale''': 7.5,
                 '''output_type''': '''numpy''',
             }
             return inputs


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[int]	=		'''stabilityai/stable-diffusion-2-base'''
             _lowerCamelCase							:     Tuple	=		DDIMScheduler.from_pretrained(a__      ,							subfolder='''scheduler''')
             _lowerCamelCase							:     int	=		StableDiffusionPanoramaPipeline.from_pretrained(a__      ,							scheduler=a__      ,							safety_checker=a__)
             pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)
             pipe.enable_attention_slicing()

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

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

             _lowerCamelCase							:     Union[str, Any]	=		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 				__snake_case			(		self):



             """simple docstring"""





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

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

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

             _lowerCamelCase							:     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 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		0

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

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

                           _lowerCamelCase							:     Dict	=		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

             _lowerCamelCase							:     List[Any]	=		False

             _lowerCamelCase							:     int	=		'''stabilityai/stable-diffusion-2-base'''
             _lowerCamelCase							:     str	=		DDIMScheduler.from_pretrained(a__      ,							subfolder='''scheduler''')
             _lowerCamelCase							:     str	=		StableDiffusionPanoramaPipeline.from_pretrained(a__      ,							scheduler=a__      ,							safety_checker=a__)
             _lowerCamelCase							:     Optional[int]	=		pipe.to(a__)
             pipe.set_progress_bar_config(disable=a__)
             pipe.enable_attention_slicing()

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



      def 				__snake_case			(		self):



             """simple docstring"""





             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

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

             _lowerCamelCase							:     Dict	=		self.get_inputs()
             _lowerCamelCase							:     List[Any]	=		pipe(**a__)

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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_lowerCamelCase			       =      'Create a default config file for Accelerate with only a few flags set.'




def    __UpperCAmelCase(      lowercase_="no" ,    lowercase_ = default_json_config_file ,    lowercase_ = False      ):
       _lowerCamelCase							:     List[str]	=		Path(lowercase_      )
       path.parent.mkdir(parents=lowercase_ ,    exist_ok=lowercase_      )
       if path.exists():
              print(
                  F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""      )
              return False
       _lowerCamelCase							:     List[Any]	=		mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""      )
       _lowerCamelCase							:     Optional[int]	=		{
           '''compute_environment''': '''LOCAL_MACHINE''',
           '''mixed_precision''': mixed_precision,
       }
       if torch.cuda.is_available():
              _lowerCamelCase							:     Any	=		torch.cuda.device_count()
              _lowerCamelCase							:     str	=		num_gpus
              _lowerCamelCase							:     Union[str, Any]	=		False
              if num_gpus > 1:
                     _lowerCamelCase							:     str	=		'''MULTI_GPU'''
              else:
                     _lowerCamelCase							:     List[str]	=		'''NO'''
       elif is_xpu_available() and use_xpu:
              _lowerCamelCase							:     Union[str, Any]	=		torch.xpu.device_count()
              _lowerCamelCase							:     int	=		num_xpus
              _lowerCamelCase							:     Dict	=		False
              if num_xpus > 1:
                     _lowerCamelCase							:     List[str]	=		'''MULTI_XPU'''
              else:
                     _lowerCamelCase							:     Any	=		'''NO'''
       elif is_npu_available():
              _lowerCamelCase							:     List[str]	=		torch.npu.device_count()
              _lowerCamelCase							:     Optional[int]	=		num_npus
              _lowerCamelCase							:     str	=		False
              if num_npus > 1:
                     _lowerCamelCase							:     List[Any]	=		'''MULTI_NPU'''
              else:
                     _lowerCamelCase							:     str	=		'''NO'''
       else:
              _lowerCamelCase							:     Tuple	=		0
              _lowerCamelCase							:     Optional[Any]	=		True
              _lowerCamelCase							:     List[str]	=		1
              _lowerCamelCase							:     Union[str, Any]	=		'''NO'''
       _lowerCamelCase							:     Any	=		ClusterConfig(**lowercase_      )
       config.to_json_file(lowercase_      )
       return path




def    __UpperCAmelCase(      lowercase_ ,    lowercase_      ):
       _lowerCamelCase							:     List[str]	=		parser.add_parser('''default''' ,    parents=lowercase_ ,    help=lowercase_ ,    formatter_class=lowercase_      )
       parser.add_argument(
           '''--config_file''' ,    default=lowercase_ ,    help=(
               '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
               '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
               '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
               '''with \'huggingface\'.'''
           ) ,    dest='''save_location''' ,    )

       parser.add_argument(
           '''--mixed_precision''' ,    choices=['''no''', '''fp16''', '''bf16'''] ,    type=lowercase_ ,    help='''Whether or not to use mixed precision training. '''
           '''Choose between FP16 and BF16 (bfloat16) training. '''
           '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.''' ,    default='''no''' ,    )
       parser.set_defaults(func=lowercase_      )
       return parser




def    __UpperCAmelCase(      lowercase_      ):
       _lowerCamelCase							:     str	=		write_basic_config(args.mixed_precision ,    args.save_location      )
       if config_file:
              print(F"""accelerate configuration saved at {config_file}"""      )

