
"""simple docstring"""




from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings



@maybe_allow_in_graph
class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=0.0   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = "geglu"   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = False   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = "layer_norm"   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   only_cross_attention

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   (num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

											if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
																		raise ValueError(
																		    F'''`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'''
																		    F''' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.'''    )

											# Define 3 blocks. Each block has its own normalization layer.
											# 1. Self-Attn
											if self.use_ada_layer_norm:
																		SCREAMING_SNAKE_CASE__						:       int           =   AdaLayerNorm(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   AdaLayerNormZero(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   Attention(
											    query_dim=SCREAMING_SNAKE_CASE__   ,       heads=SCREAMING_SNAKE_CASE__   ,       dim_head=SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       bias=SCREAMING_SNAKE_CASE__   ,       cross_attention_dim=cross_attention_dim if only_cross_attention else None   ,       upcast_attention=SCREAMING_SNAKE_CASE__   ,       )

											# 2. Cross-Attn
											if cross_attention_dim is not None or double_self_attention:
																		# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
																		# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
																		# the second cross attention block.
																		SCREAMING_SNAKE_CASE__						:       Any           =   (
																		    AdaLayerNorm(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																		    if self.use_ada_layer_norm
																		    else nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
																		)
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   Attention(
																		    query_dim=SCREAMING_SNAKE_CASE__   ,       cross_attention_dim=cross_attention_dim if not double_self_attention else None   ,       heads=SCREAMING_SNAKE_CASE__   ,       dim_head=SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       bias=SCREAMING_SNAKE_CASE__   ,       upcast_attention=SCREAMING_SNAKE_CASE__   ,       )  # is self-attn if encoder_hidden_states is none
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   None
																		SCREAMING_SNAKE_CASE__						:       str           =   None

											# 3. Feed-forward
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FeedForward(SCREAMING_SNAKE_CASE__   ,       dropout=SCREAMING_SNAKE_CASE__   ,       activation_fn=SCREAMING_SNAKE_CASE__   ,       final_dropout=SCREAMING_SNAKE_CASE__    )

											# let chunk size default to None
											SCREAMING_SNAKE_CASE__						:       Tuple           =   None
											SCREAMING_SNAKE_CASE__						:       Dict           =   0



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   chunk_size
											SCREAMING_SNAKE_CASE__						:       str           =   dim



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       )					->		Any:



											"""simple docstring"""




											if self.use_ada_layer_norm:
																		SCREAMING_SNAKE_CASE__						:       Any           =   self.norma(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.norma(
																		    SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       hidden_dtype=hidden_states.dtype    )
											else:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.norma(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       int           =   cross_attention_kwargs if cross_attention_kwargs is not None else {}

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.attna(
											    SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )
											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   gate_msa.unsqueeze(1    ) * attn_output
											SCREAMING_SNAKE_CASE__						:       Dict           =   attn_output + hidden_states

											# 2. Cross-Attention
											if self.attna is not None:
																		SCREAMING_SNAKE_CASE__						:       str           =   (
																		    self.norma(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) if self.use_ada_layer_norm else self.norma(SCREAMING_SNAKE_CASE__    )
																		)

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.attna(
																		    SCREAMING_SNAKE_CASE__   ,       encoder_hidden_states=SCREAMING_SNAKE_CASE__   ,       attention_mask=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__   ,       )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   attn_output + hidden_states

											# 3. Feed-forward
											SCREAMING_SNAKE_CASE__						:       Any           =   self.norma(SCREAMING_SNAKE_CASE__    )

											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

											if self._chunk_size is not None:
																		# "feed_forward_chunk_size" can be used to save memory
																		if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																									raise ValueError(
																									    F'''`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.'''    )

																		SCREAMING_SNAKE_CASE__						:       List[str]           =   norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
																		SCREAMING_SNAKE_CASE__						:       int           =   torch.cat(
																		    [self.ff(SCREAMING_SNAKE_CASE__    ) for hid_slice in norm_hidden_states.chunk(SCREAMING_SNAKE_CASE__   ,       dim=self._chunk_dim    )]   ,       dim=self._chunk_dim   ,       )
											else:
																		SCREAMING_SNAKE_CASE__						:       int           =   self.ff(SCREAMING_SNAKE_CASE__    )

											if self.use_ada_layer_norm_zero:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   gate_mlp.unsqueeze(1    ) * ff_output

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ff_output + hidden_states

											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 4   ,       SCREAMING_SNAKE_CASE__ = 0.0   ,       SCREAMING_SNAKE_CASE__ = "geglu"   ,       SCREAMING_SNAKE_CASE__ = False   ,       )					->		Tuple:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(dim * mult    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   dim_out if dim_out is not None else dim

											if activation_fn == "gelu":
																		SCREAMING_SNAKE_CASE__						:       str           =   GELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											if activation_fn == "gelu-approximate":
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   GELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       approximate="""tanh"""    )
											elif activation_fn == "geglu":
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   GEGLU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											elif activation_fn == "geglu-approximate":
																		SCREAMING_SNAKE_CASE__						:       int           =   ApproximateGELU(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Any           =   nn.ModuleList([]    )
											# project in
											self.net.append(SCREAMING_SNAKE_CASE__    )
											# project dropout
											self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__    )    )
											# project out
											self.net.append(nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )    )
											# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
											if final_dropout:
																		self.net.append(nn.Dropout(SCREAMING_SNAKE_CASE__    )    )



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



											"""simple docstring"""




											for module in self.net:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   module(SCREAMING_SNAKE_CASE__    )
											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = "none"    )					->		List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Any           =   approximate



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[int]:



											"""simple docstring"""




											if gate.device.type != "mps":
																		return F.gelu(SCREAMING_SNAKE_CASE__   ,       approximate=self.approximate    )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa    )   ,       approximate=self.approximate    ).to(dtype=gate.dtype    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   self.proj(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.gelu(SCREAMING_SNAKE_CASE__    )
											return hidden_states






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       Dict           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       dim_out * 2    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											if gate.device.type != "mps":
																		return F.gelu(SCREAMING_SNAKE_CASE__    )
											# mps: gelu is not implemented for float16
											return F.gelu(gate.to(dtype=torch.floataa    )    ).to(dtype=gate.dtype    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.proj(SCREAMING_SNAKE_CASE__    ).chunk(2   ,       dim=-1    )
											return hidden_states * self.gelu(SCREAMING_SNAKE_CASE__    )






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       str           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.proj(SCREAMING_SNAKE_CASE__    )
											return x * torch.sigmoid(1.702 * x    )






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Optional[Any]:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Embedding(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   nn.SiLU()
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       embedding_dim * 2    )
											SCREAMING_SNAKE_CASE__						:       List[str]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__    )    )    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   torch.chunk(SCREAMING_SNAKE_CASE__   ,       2    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.norm(SCREAMING_SNAKE_CASE__    ) * (1 + scale) + shift
											return x






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		int:



											"""simple docstring"""




											super().__init__()

											SCREAMING_SNAKE_CASE__						:       Any           =   CombinedTimestepLabelEmbeddings(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   nn.SiLU()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       6 * embedding_dim   ,       bias=SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.LayerNorm(SCREAMING_SNAKE_CASE__   ,       elementwise_affine=SCREAMING_SNAKE_CASE__   ,       eps=1E-6    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=None    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.linear(self.silu(self.emb(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       hidden_dtype=SCREAMING_SNAKE_CASE__    )    )    )
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       int           =   emb.chunk(6   ,       dim=1    )
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.norm(SCREAMING_SNAKE_CASE__    ) * (1 + scale_msa[:, None]) + shift_msa[:, None]
											return x, gate_msa, shift_mlp, scale_mlp, gate_mlp






class 	lowerCAmelCase_						(nn.Module  ):


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = 1E-5    )					->		int:



											"""simple docstring"""




											super().__init__()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   num_groups
											SCREAMING_SNAKE_CASE__						:       Any           =   eps

											if act_fn is None:
																		SCREAMING_SNAKE_CASE__						:       Any           =   None
											else:
																		SCREAMING_SNAKE_CASE__						:       Dict           =   get_activation(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   nn.Linear(SCREAMING_SNAKE_CASE__   ,       out_dim * 2    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											if self.act:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.act(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.linear(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   emb[:, :, None, None]
											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   emb.chunk(2   ,       dim=1    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   F.group_norm(SCREAMING_SNAKE_CASE__   ,       self.num_groups   ,       eps=self.eps    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   x * (1 + scale) + shift
											return x







import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



