import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict




@pytest.mark.parametrize(
    'files'						,[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]						,)
def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			Any     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				tmp_path_factory.mktemp('dset_infos_dir'     )
		if "full:README.md" in files:
				with open(dataset_infos_dir / 'README.md'						,'w'     ) as f:
						f.write('---\ndataset_info:\n  dataset_size: 42\n---'     )
		if "empty:README.md" in files:
				with open(dataset_infos_dir / 'README.md'						,'w'     ) as f:
						f.write(''     )
    # we want to support dataset_infos.json for backward compatibility
		if "dataset_infos.json" in files:
				with open(dataset_infos_dir / 'dataset_infos.json'						,'w'     ) as f:
						f.write('{"default": {"dataset_size": 42}}'     )
		A_		:    Optional[int]         =				DatasetInfosDict.from_directory(__lowercase     )
		assert dataset_infos
		assert dataset_infos["default"].dataset_size == 42




@pytest.mark.parametrize(
    'dataset_info'						,[
        DatasetInfo(),
        DatasetInfo(
            description='foo'						,features=Features({'a': Value('int32'     )}     )						,builder_name='builder'						,config_name='config'						,version='1.0.0'						,splits=[{'name': 'train'}]						,download_size=42						,),
    ]						,)
def     UpperCamelCase						(	__lowercase						:			Optional[Any]						,__lowercase						:			DatasetInfo     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				str(__lowercase     )
		dataset_info.write_to_directory(__lowercase     )
		A_		:    str         =				DatasetInfo.from_directory(__lowercase     )
		assert dataset_info == reloaded
		assert os.path.exists(os.path.join(__lowercase						,'dataset_info.json'     )     )




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Optional[Any]         =				DatasetInfo(
		    description='foo'						,citation='bar'						,homepage='https://foo.bar'						,license='CC0'						,features=Features({'a': Value('int32'     )}     )						,post_processed={}						,supervised_keys=()						,task_templates=[]						,builder_name='builder'						,config_name='config'						,version='1.0.0'						,splits=[{'name': 'train', 'num_examples': 42}]						,download_checksums={}						,download_size=13_37						,post_processing_size=4_42						,dataset_size=12_34						,size_in_bytes=13_37 + 4_42 + 12_34						,)
		A_		:    Tuple         =				dataset_info._to_yaml_dict()
		assert sorted(__lowercase     ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML     )
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				assert key in dataset_info_yaml_dict
				assert isinstance(dataset_info_yaml_dict[key]						,(list, dict, int, str)     )
		A_		:    Dict         =				yaml.safe_dump(__lowercase     )
		A_		:    Tuple         =				yaml.safe_load(__lowercase     )
		assert dataset_info_yaml_dict == reloaded




def     UpperCamelCase						(	):
		'''simple docstring'''




		A_		:    Any         =				DatasetInfo()
		A_		:    Optional[Any]         =				dataset_info._to_yaml_dict()
		assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    'dataset_infos_dict'						,[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}     ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}     ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'						,features=Features({'a': Value('int32'     )}     )						,builder_name='builder'						,config_name='config'						,version='1.0.0'						,splits=[{'name': 'train'}]						,download_size=42						,)
            }     ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42     ),
                'v2': DatasetInfo(dataset_size=13_37     ),
            }     ),
    ]						,)
def     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			DatasetInfosDict     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				str(__lowercase     )
		dataset_infos_dict.write_to_directory(__lowercase     )
		A_		:    Optional[int]         =				DatasetInfosDict.from_directory(__lowercase     )

		# the config_name of the dataset_infos_dict take over the attribute
		for config_name, dataset_info in dataset_infos_dict.items():
				A_		:    Any         =				config_name
				# the yaml representation doesn't include fields like description or citation
				# so we just test that we can recover what we can from the yaml
				A_		:    Optional[Any]         =				DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()     )
		assert dataset_infos_dict == reloaded

		if dataset_infos_dict:
				assert os.path.exists(os.path.join(__lowercase						,'README.md'     )     )







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    int         =				UNetaDModel(
						    block_out_channels=(3_2, 6_4)		,     layers_per_block=2		,     sample_size=3_2		,     in_channels=3		,     out_channels=3		,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    Optional[Any]         =				VQModel(
						    block_out_channels=[3_2, 6_4]		,     in_channels=3		,     out_channels=3		,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,     latent_channels=3		,     )
						return model
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						torch.manual_seed(0 )
						A_		:    List[Any]         =				CLIPTextConfig(
						    bos_token_id=0		,     eos_token_id=2		,     hidden_size=3_2		,     intermediate_size=3_7		,     layer_norm_eps=1E-05		,     num_attention_heads=4		,     num_hidden_layers=5		,     pad_token_id=1		,     vocab_size=1_0_0_0		,     )
						return CLIPTextModel(lowercase )







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[int]         =				self.dummy_uncond_unet
						A_		:    List[Any]         =				DDIMScheduler()
						A_		:    Any         =				self.dummy_vq_model

						A_		:    int         =				LDMPipeline(unet=lowercase		,     vqvae=lowercase		,     scheduler=lowercase )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    Dict         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy' ).images

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=2		,     output_type='numpy'		,     return_dict=lowercase )[0]

						A_		:    Union[str, Any]         =				image[0, -3:, -3:, -1]
						A_		:    int         =				image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						A_		:    List[str]         =				np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172] )
						A_		:    str         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance





@slow
@require_torch
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[Any]         =				LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256' )
						ldm.to(lowercase )
						ldm.set_progress_bar_config(disable=lowercase )

						A_		:    Any         =				torch.manual_seed(0 )
						A_		:    List[str]         =				ldm(generator=lowercase		,     num_inference_steps=5		,     output_type='numpy' ).images

						A_		:    List[Any]         =				image[0, -3:, -3:, -1]

						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						A_		:    Tuple         =				np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447] )
						A_		:    Dict         =				1E-2 if torch_device != 'mps' else 3E-2

						assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance






