



"""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


A:   str								=		logging.get_logger(__name__)

enable_full_determinism()



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Optional[Any]						=						UNetaDModel
   __lowerCAmelCase  :   Tuple						=						'sample'

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	4
    UpperCAmelCase	:					Dict	=	3
    UpperCAmelCase	:					Optional[Any]	=	(32, 32)

    UpperCAmelCase	:					Optional[int]	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[str]	=	torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return (3, 32, 32)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    return (3, 32, 32)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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,
    }
    UpperCAmelCase	:					List[str]	=	self.dummy_input
    return init_dict, inputs_dict



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   int						=						UNetaDModel
   __lowerCAmelCase  :   List[str]						=						'sample'

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					str	=	4
    UpperCAmelCase	:					str	=	4
    UpperCAmelCase	:					Any	=	(32, 32)

    UpperCAmelCase	:					int	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	torch.tensor([10]						).to(_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return (4, 32, 32)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return (4, 32, 32)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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"""),
    }
    UpperCAmelCase	:					Tuple	=	self.dummy_input
    return init_dict, inputs_dict

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)

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

    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Dict	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    model_accelerate.to(_SCREAMING_SNAKE_CASE						)
    model_accelerate.eval()

    UpperCAmelCase	:					Union[str, Any]	=	torch.randn(
        1      ,     model_accelerate.config.in_channels      ,     model_accelerate.config.sample_size      ,     model_accelerate.config.sample_size      ,     generator=torch.manual_seed(0						)      ,     )
    UpperCAmelCase	:					Dict	=	noise.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[int]	=	torch.tensor([10] * noise.shape[0]						).to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					int	=	model_accelerate(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)["""sample"""]

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

    UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	UNetaDModel.from_pretrained(
        """fusing/unet-ldm-dummy-update"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE      ,     low_cpu_mem_usage=_SCREAMING_SNAKE_CASE						)
    model_normal_load.to(_SCREAMING_SNAKE_CASE						)
    model_normal_load.eval()
    UpperCAmelCase	:					int	=	model_normal_load(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)["""sample"""]

    assert torch_all_close(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-3						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""						)
    model.eval()
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Tuple	=	torch.randn(
        1      ,     model.config.in_channels      ,     model.config.sample_size      ,     model.config.sample_size      ,     generator=torch.manual_seed(0						)      ,     )
    UpperCAmelCase	:					Optional[Any]	=	noise.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	torch.tensor([10] * noise.shape[0]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					List[Any]	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Tuple	=	output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					List[Any]	=	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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-3						)						)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						UNetaDModel
   __lowerCAmelCase  :   List[str]						=						'sample'

   @property
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=(32, 32)						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	4
    UpperCAmelCase	:					Tuple	=	3

    UpperCAmelCase	:					List[str]	=	floats_tensor((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	torch.tensor(batch_size * [10]						).to(dtype=torch.intaa      ,     device=_SCREAMING_SNAKE_CASE						)

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

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    return (3, 32, 32)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return (3, 32, 32)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{
        """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""",
        ],
    }
    UpperCAmelCase	:					Optional[Any]	=	self.dummy_input
    return init_dict, inputs_dict

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	,		UpperCAmelCase	:					List[Any]	=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""      ,     output_loading_info=_SCREAMING_SNAKE_CASE						)
    self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)
    self.assertEqual(len(loading_info["""missing_keys"""]						)      ,     0						)

    model.to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	self.dummy_input
    UpperCAmelCase	:					int	=	floats_tensor((4, 3) + (256, 256)						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	noise
    UpperCAmelCase	:					Optional[int]	=	model(**_SCREAMING_SNAKE_CASE						)

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

   @slow
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""						)
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Optional[Any]	=	4
    UpperCAmelCase	:					Optional[Any]	=	3
    UpperCAmelCase	:					List[Any]	=	(256, 256)

    UpperCAmelCase	:					int	=	torch.ones((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	torch.tensor(batch_size * [1E-4]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					Dict	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					str	=	output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					Optional[int]	=	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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-2						)						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""						)
    model.to(_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	4
    UpperCAmelCase	:					Optional[int]	=	3
    UpperCAmelCase	:					Optional[Any]	=	(32, 32)

    UpperCAmelCase	:					List[Any]	=	torch.ones((batch_size, num_channels) + sizes						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	torch.tensor(batch_size * [1E-4]						).to(_SCREAMING_SNAKE_CASE						)

    with torch.no_grad():
     UpperCAmelCase	:					int	=	model(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						).sample

    UpperCAmelCase	:					Any	=	output[0, -3:, -3:, -1].flatten().cpu()
    # fmt: off
    UpperCAmelCase	:					int	=	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(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     rtol=1E-2						)						)




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    pass




'''simple docstring'''



import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__lowercase	:						Tuple       =	pytest.mark.integration


__lowercase	:						Optional[int]       =	{'comet'}
__lowercase	:						List[str]       =	importlib.util.find_spec('fairseq') is not None

__lowercase	:						str       =	{'code_eval'}
__lowercase	:						List[Any]       =	os.name == 'nt'

__lowercase	:						Optional[Any]       =	{'bertscore', 'frugalscore', 'perplexity'}
__lowercase	:						Optional[Any]       =	importlib.util.find_spec('transformers') is not None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   int						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
										if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
															self.skipTest('"test requires Fairseq"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
										if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
															self.skipTest('"test requires transformers"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					@wraps(_SCREAMING_SNAKE_CASE   )
					def wrapper(self      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
										if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
															self.skipTest('"test not supported on Windows"'   )
										else:
															test_case(self						,      _SCREAMING_SNAKE_CASE   )

					return wrapper






def   lowerCamelCase							():
					__a			:       List[Any]					    =  [metric_dir.split(os.sep   )[-2] for metric_dir in glob.glob('./metrics/*/'   )]
					return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished





@parameterized.named_parameters(get_local_metric_names()					)
@for_all_test_methods(
    lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_					)
@local
class 							__UpperCamelCase				(		parameterized.TestCase					):
	A_				=    {}
	A_				=    None




	@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'							)
	@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'							)
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       int					    =  '[...]'
						__a			:       Tuple					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						__a			:       Optional[Any]					    =  datasets.load.import_main_class(metric_module.__name__						,				dataset=__a							)
						# check parameters
						__a			:       Dict					    =  inspect.signature(metric._compute							).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()							)							)  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(__a						,				metric_module.__name__							):
											with self.use_local_metrics():
																try:
																					__a			:       str					    =  doctest.testmod(__a						,				verbose=__a						,				raise_on_error=__a							)
																except doctest.UnexpectedException as e:
																					raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed						,				0							)
						self.assertGreater(results.attempted						,				1							)




	@slow
	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						__a			:       Tuple					    =  '[...]'
						__a			:       Optional[Any]					    =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,				__a							)							).module_path							)
						# run doctest
						with self.use_local_metrics():
											__a			:       List[Any]					    =  doctest.testmod(__a						,				verbose=__a						,				raise_on_error=__a							)
						self.assertEqual(results.failed						,				0							)
						self.assertGreater(results.attempted						,				1							)




	@contextmanager
	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						if metric_name in self.INTENSIVE_CALLS_PATCHER:
											with self.INTENSIVE_CALLS_PATCHER[metric_name](__a							):
																yield
						else:
											yield




	@contextmanager
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						def load_local_metric(__a						,				*__a						,				**__a							):
											return load_metric(os.path.join('metrics'						,				__a							)						,				*__a						,				**__a							)

						with patch('datasets.load_metric'							) as mock_load_metric:
											__a			:       Dict					    =  load_local_metric
											yield





	@classmethod
	def       __UpperCAmelCase							(   cls						,				__a							):

						'''simple docstring'''





						def wrapper(__a							):
											__a			:       Optional[Any]					    =  contextmanager(__a							)
											__a			:       str					    =  patcher
											return patcher

						return wrapper






@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					import tensorflow.compat.va as tf
					from bleurt.score import Predictor

					tf.flags.DEFINE_string('sv'						,      ''						,      ''   )  # handle pytest cli flags





					class 							__UpperCamelCase				(		lowerCAmelCase_					):




						def       __UpperCAmelCase							(   self						,				__a							):

											'''simple docstring'''





											assert len(input_dict['input_ids']							) == 2
											return np.array([1.03, 1.04]							)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
					with patch('bleurt.score._create_predictor'   ) as mock_create_predictor:
										__a			:       Dict					    =  MockedPredictor()
										yield






@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					import torch

					def bert_cos_score_idf(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   str						,      *_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
										return torch.tensor([[1.0, 1.0, 1.0]] * len(_SCREAMING_SNAKE_CASE   )   )

					# mock get_model which is supposed to do download a bert model
					# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
					with patch('bert_score.scorer.get_model'   ), patch(
					    'bert_score.scorer.bert_cos_score_idf'   ) as mock_bert_cos_score_idf:
										__a			:       str					    =  bert_cos_score_idf
										yield






@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Dict   ):
					def load_from_checkpoint(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):





										class 							__UpperCamelCase				:




											def       __UpperCAmelCase							(   self						,				__a						,				*__a						,				**__a							):

																'''simple docstring'''





																assert len(__a							) == 2
																__a			:       Dict					    =  [0.19, 0.92]
																return scores, sum(__a							) / len(__a							)



										return Model()

					# mock load_from_checkpoint which is supposed to do download a bert model
					# mock load_from_checkpoint which is supposed to do download a bert model
					with patch('comet.download_model'   ) as mock_download_model:
										__a			:       str					    =  None
										with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
															__a			:       int					    =  load_from_checkpoint
															yield






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  load_metric(os.path.join('metrics'						,      'seqeval'   )   )
					__a			:       List[str]					    =  'ERROR'
					__a			:       List[str]					    =  F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
					with pytest.raises(_SCREAMING_SNAKE_CASE						,      match=re.escape(_SCREAMING_SNAKE_CASE   )   ):
										metric.compute(predictions=[]						,      references=[]						,      scheme=_SCREAMING_SNAKE_CASE   )



