'''simple docstring'''

import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowerCAmelCase_ : str						  =      logging.get_logger(__name__)

enable_full_determinism()





class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=UNetaDModel
   lowerCamelCase_    					='sample'


   @property
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[int]:
         lowercase_     =      4
         lowercase_     =      3
         lowercase_     =      (32, 32)

         lowercase_     =      floats_tensor((batch_size, num_channels) + sizes).to(__lowerCAmelCase)
         lowercase_     =      torch.tensor([10]).to(__lowerCAmelCase)

         return {"sample": noise, "timestep": time_step}


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					int:
         return (3, 32, 32)


   @property
   def __UpperCAmelCase       (   self				:	List[str])      ->					int:
         return (3, 32, 32)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Union[str, Any]:
         lowercase_     =      {
             "block_out_channels": (32, 64),
             "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
             "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
             "attention_head_dim": 3,
             "out_channels": 3,
             "in_channels": 3,
             "layers_per_block": 2,
             "sample_size": 32,
         }
         lowercase_     =      self.dummy_input
         return init_dict, inputs_dict





class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=UNetaDModel
   lowerCamelCase_    					='sample'


   @property
   def __UpperCAmelCase       (   self				:	List[str])      ->					List[Any]:
         lowercase_     =      4
         lowercase_     =      4
         lowercase_     =      (32, 32)

         lowercase_     =      floats_tensor((batch_size, num_channels) + sizes).to(__lowerCAmelCase)
         lowercase_     =      torch.tensor([10]).to(__lowerCAmelCase)

         return {"sample": noise, "timestep": time_step}


   @property
   def __UpperCAmelCase       (   self				:	Any)      ->					Any:
         return (4, 32, 32)


   @property
   def __UpperCAmelCase       (   self				:	Any)      ->					Optional[Any]:
         return (4, 32, 32)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      {
             "sample_size": 32,
             "in_channels": 4,
             "out_channels": 4,
             "layers_per_block": 2,
             "block_out_channels": (32, 64),
             "attention_head_dim": 32,
             "down_block_types": ("DownBlock2D", "DownBlock2D"),
             "up_block_types": ("UpBlock2D", "UpBlock2D"),
         }
         lowercase_     =      self.dummy_input
         return init_dict, inputs_dict


   def __UpperCAmelCase       (   self				:	Dict)      ->					Union[str, Any]:
         lowercase_ ,							lowercase_     =      UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   ,		output_loading_info=__lowerCAmelCase)

         self.assertIsNotNone(__lowerCAmelCase)
         self.assertEqual(len(loading_info["missing_keys"])   ,		0)

         model.to(__lowerCAmelCase)
         lowercase_     =      model(**self.dummy_input).sample

         assert image is not None, "Make sure output is not None"


   @unittest.skipIf(torch_device != "cuda"   ,		"This test is supposed to run on GPU")
   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   ,		output_loading_info=__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         lowercase_     =      model(**self.dummy_input).sample

         assert image is not None, "Make sure output is not None"


   @unittest.skipIf(torch_device != "cuda"   ,		"This test is supposed to run on GPU")
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
         lowercase_ ,							lowercase_     =      UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"   ,		output_loading_info=__lowerCAmelCase)
         model_accelerate.to(__lowerCAmelCase)
         model_accelerate.eval()

         lowercase_     =      torch.randn(
             1   ,		model_accelerate.config.in_channels   ,		model_accelerate.config.sample_size   ,		model_accelerate.config.sample_size   ,		generator=torch.manual_seed(0)   ,		)
         lowercase_     =      noise.to(__lowerCAmelCase)
         lowercase_     =      torch.tensor([10] * noise.shape[0]).to(__lowerCAmelCase)

         lowercase_     =      model_accelerate(__lowerCAmelCase   ,		__lowerCAmelCase)["sample"]

         # two models don't need to stay in the device at the same time
         del model_accelerate
         torch.cuda.empty_cache()
         gc.collect()

         lowercase_ ,							lowercase_     =      UNetaDModel.from_pretrained(
             "fusing/unet-ldm-dummy-update"   ,		output_loading_info=__lowerCAmelCase   ,		low_cpu_mem_usage=__lowerCAmelCase)
         model_normal_load.to(__lowerCAmelCase)
         model_normal_load.eval()
         lowercase_     =      model_normal_load(__lowerCAmelCase   ,		__lowerCAmelCase)["sample"]

         assert torch_all_close(__lowerCAmelCase   ,		__lowerCAmelCase   ,		rtol=1e-3)


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         lowercase_     =      UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update")
         model.eval()
         model.to(__lowerCAmelCase)

         lowercase_     =      torch.randn(
             1   ,		model.config.in_channels   ,		model.config.sample_size   ,		model.config.sample_size   ,		generator=torch.manual_seed(0)   ,		)
         lowercase_     =      noise.to(__lowerCAmelCase)
         lowercase_     =      torch.tensor([10] * noise.shape[0]).to(__lowerCAmelCase)

         with torch.no_grad():
               lowercase_     =      model(__lowerCAmelCase   ,		__lowerCAmelCase).sample

         lowercase_     =      output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         lowercase_     =      torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
         # fmt: on

         self.assertTrue(torch_all_close(__lowerCAmelCase   ,		__lowerCAmelCase   ,		rtol=1e-3))





class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=UNetaDModel
   lowerCamelCase_    					='sample'


   @property
   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]=(32, 32))      ->					List[str]:
         lowercase_     =      4
         lowercase_     =      3

         lowercase_     =      floats_tensor((batch_size, num_channels) + sizes).to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(batch_size * [10]).to(dtype=torch.intaa   ,		device=__lowerCAmelCase)

         return {"sample": noise, "timestep": time_step}


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					Union[str, Any]:
         return (3, 32, 32)


   @property
   def __UpperCAmelCase       (   self				:	Dict)      ->					Optional[Any]:
         return (3, 32, 32)


   def __UpperCAmelCase       (   self				:	List[Any])      ->					List[Any]:
         lowercase_     =      {
             "block_out_channels": [32, 64, 64, 64],
             "in_channels": 3,
             "layers_per_block": 1,
             "out_channels": 3,
             "time_embedding_type": "fourier",
             "norm_eps": 1e-6,
             "mid_block_scale_factor": math.sqrt(2.0),
             "norm_num_groups": None,
             "down_block_types": [
                 "SkipDownBlock2D",
                 "AttnSkipDownBlock2D",
                 "SkipDownBlock2D",
                 "SkipDownBlock2D",
             ],
             "up_block_types": [
                 "SkipUpBlock2D",
                 "SkipUpBlock2D",
                 "AttnSkipUpBlock2D",
                 "SkipUpBlock2D",
             ],
         }
         lowercase_     =      self.dummy_input
         return init_dict, inputs_dict


   @slow
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					str:
         lowercase_ ,							lowercase_     =      UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"   ,		output_loading_info=__lowerCAmelCase)
         self.assertIsNotNone(__lowerCAmelCase)
         self.assertEqual(len(loading_info["missing_keys"])   ,		0)

         model.to(__lowerCAmelCase)
         lowercase_     =      self.dummy_input
         lowercase_     =      floats_tensor((4, 3) + (256, 256)).to(__lowerCAmelCase)
         lowercase_     =      noise
         lowercase_     =      model(**__lowerCAmelCase)

         assert image is not None, "Make sure output is not None"


   @slow
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Any:
         lowercase_     =      UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256")
         model.to(__lowerCAmelCase)

         lowercase_     =      4
         lowercase_     =      3
         lowercase_     =      (256, 256)

         lowercase_     =      torch.ones((batch_size, num_channels) + sizes).to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(batch_size * [1e-4]).to(__lowerCAmelCase)

         with torch.no_grad():
               lowercase_     =      model(__lowerCAmelCase   ,		__lowerCAmelCase).sample

         lowercase_     =      output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         lowercase_     =      torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608])
         # fmt: on

         self.assertTrue(torch_all_close(__lowerCAmelCase   ,		__lowerCAmelCase   ,		rtol=1e-2))


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Union[str, Any]:
         lowercase_     =      UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
         model.to(__lowerCAmelCase)

         lowercase_     =      4
         lowercase_     =      3
         lowercase_     =      (32, 32)

         lowercase_     =      torch.ones((batch_size, num_channels) + sizes).to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(batch_size * [1e-4]).to(__lowerCAmelCase)

         with torch.no_grad():
               lowercase_     =      model(__lowerCAmelCase   ,		__lowerCAmelCase).sample

         lowercase_     =      output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         lowercase_     =      torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
         # fmt: on

         self.assertTrue(torch_all_close(__lowerCAmelCase   ,		__lowerCAmelCase   ,		rtol=1e-2))


   def __UpperCAmelCase       (   self				:	Any)      ->					Tuple:
         # not required for this model
         pass







'''simple docstring'''

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






