




import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				int:
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[int]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				int:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_3", "col_1", "col_2"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				Optional[int]:
						# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
						_UpperCAmelCase      =			{"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
						_UpperCAmelCase      =			features.copy()
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     features=snake_case			,     cache_dir=snake_case       ).read()
						assert isinstance(snake_case			,     snake_case       )
						assert dataset.num_rows == 2
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_2", "col_3", "col_1"]
						for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case			,     split=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )
						assert dataset.split == split if split else "train"




@pytest.mark.parametrize("""path_type"""			,     [str, list]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Any:
						if issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			jsonl_path
						elif issubclass(snake_case			,     snake_case       ):
												_UpperCAmelCase      =			[jsonl_path]
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_dataset(snake_case			,     snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=("train",)       )						->				str:
						assert isinstance(snake_case			,     snake_case       )
						for split in splits:
												_UpperCAmelCase      =			dataset_dict[split]
												assert dataset.num_rows == 4
												assert dataset.num_columns == 3
												assert dataset.column_names == ["col_1", "col_2", "col_3"]
												for feature, expected_dtype in expected_features.items():
																		assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize("""keep_in_memory"""			,     [False, True]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				str:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     cache_dir=snake_case			,     keep_in_memory=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize(
    """features"""			,     [
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]			,     )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Tuple:
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			features.copy() if features else default_expected_features
						_UpperCAmelCase      =			(
						    Features({feature: Value(snake_case       ) for feature, dtype in features.items()}       ) if features is not None else None
						)
						_UpperCAmelCase      =			JsonDatasetReader({"""train""": jsonl_path}			,     features=snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case       )




@pytest.mark.parametrize("""split"""			,     [None, NamedSplit("""train"""       ), """train""", """test"""]       )
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				Optional[Any]:
						if split:
												_UpperCAmelCase      =			{split: jsonl_path}
						else:
												_UpperCAmelCase      =			"""train"""
												_UpperCAmelCase      =			{"""train""": jsonl_path, """test""": jsonl_path}
						_UpperCAmelCase      =			tmp_path / """cache"""
						_UpperCAmelCase      =			{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
						_UpperCAmelCase      =			JsonDatasetReader(snake_case			,     cache_dir=snake_case       ).read()
						_check_json_datasetdict(snake_case			,     snake_case			,     splits=list(path.keys()       )       )
						assert all(dataset[split].split == split for split in path.keys()       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return json.load(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
						return [json.loads(snake_case       ) for line in buffer]




class       _A				:






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize("""lines, load_json_function"""						,		[(True, load_json_lines), (False, load_json)]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json_function(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert isinstance(exported_content[0]						,		_SCREAMING_SNAKE_CASE			)
								assert len(_SCREAMING_SNAKE_CASE			) == 10






		@pytest.mark.parametrize(
		    """orient, container, keys, len_at"""						,		[
		        ("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
		        ("""split""", dict, {"""columns""", """data"""}, """data"""),
		        ("""index""", dict, set("""0123456789"""			), None),
		        ("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
		        ("""values""", list, None, None),
		        ("""table""", dict, {"""schema""", """data"""}, """data"""),
		    ]						,		)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								with io.BytesIO() as buffer:
														JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		lines=_SCREAMING_SNAKE_CASE						,		orient=_SCREAMING_SNAKE_CASE						,		num_proc=2			).write()
														buffer.seek(0			)
														_UpperCAmelCase      =			load_json(_SCREAMING_SNAKE_CASE			)
								assert isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								if keys:
														if container is dict:
																				assert exported_content.keys() == keys
														else:
																				assert exported_content[0].keys() == keys
								else:
														assert not hasattr(_SCREAMING_SNAKE_CASE						,		"""keys"""			) and not hasattr(exported_content[0]						,		"""keys"""			)
								if len_at:
														assert len(exported_content[len_at]			) == 10
								else:
														assert len(_SCREAMING_SNAKE_CASE			) == 10






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								with pytest.raises(_SCREAMING_SNAKE_CASE			):
														with io.BytesIO() as buffer:
																				JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		num_proc=0			)






		@pytest.mark.parametrize("""compression, extension"""						,		[("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")]			)
		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			tmp_path_factory.mktemp("""data"""			) / F"test.json.{extension}"
								_UpperCAmelCase      =			str(shared_datadir / F"test_file.json.{extension}"			)
								JsonDatasetWriter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		compression=_SCREAMING_SNAKE_CASE			).write()

								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								with fsspec.open(_SCREAMING_SNAKE_CASE						,		"""rb"""						,		compression="""infer"""			) as f:
														_UpperCAmelCase      =			f.read()
								assert exported_content == original_content





from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class       _A				(		__lowercase     ):
		__a					    =       42




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE=True						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			torch.nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[0]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								# down
								_UpperCAmelCase      =			block_out_channels[0]
								for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			block_out_channels[i]
														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_down_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		add_downsample=not is_final_block						,		resnet_eps=1e-6						,		downsample_padding=0						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)
														self.down_blocks.append(_SCREAMING_SNAKE_CASE			)

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default"""						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# out
								_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[-1]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()

								_UpperCAmelCase      =			2 * out_channels if double_z else out_channels
								_UpperCAmelCase      =			nn.Convad(block_out_channels[-1]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			x
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														# down
														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				for down_block in self.down_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE			)
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE			)

								else:
														# down
														for down_block in self.down_blocks:
																				_UpperCAmelCase      =			down_block(_SCREAMING_SNAKE_CASE			)

														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE			)

								# post-process
								_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=3						,		_SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)						,		_SCREAMING_SNAKE_CASE=(64,)						,		_SCREAMING_SNAKE_CASE=2						,		_SCREAMING_SNAKE_CASE=32						,		_SCREAMING_SNAKE_CASE="silu"						,		_SCREAMING_SNAKE_CASE="group"						,		):
								super().__init__()
								_UpperCAmelCase      =			layers_per_block

								_UpperCAmelCase      =			nn.Convad(
								    _SCREAMING_SNAKE_CASE						,		block_out_channels[-1]						,		kernel_size=3						,		stride=1						,		padding=1						,		)

								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			nn.ModuleList([]			)

								_UpperCAmelCase      =			in_channels if norm_type == """spatial""" else None

								# mid
								_UpperCAmelCase      =			UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		output_scale_factor=1						,		resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type						,		attention_head_dim=block_out_channels[-1]						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		)

								# up
								_UpperCAmelCase      =			list(reversed(_SCREAMING_SNAKE_CASE			)			)
								_UpperCAmelCase      =			reversed_block_out_channels[0]
								for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE			):
														_UpperCAmelCase      =			output_channel
														_UpperCAmelCase      =			reversed_block_out_channels[i]

														_UpperCAmelCase      =			i == len(_SCREAMING_SNAKE_CASE			) - 1

														_UpperCAmelCase      =			get_up_block(
														    _SCREAMING_SNAKE_CASE						,		num_layers=self.layers_per_block + 1						,		in_channels=_SCREAMING_SNAKE_CASE						,		out_channels=_SCREAMING_SNAKE_CASE						,		prev_output_channel=_SCREAMING_SNAKE_CASE						,		add_upsample=not is_final_block						,		resnet_eps=1e-6						,		resnet_act_fn=_SCREAMING_SNAKE_CASE						,		resnet_groups=_SCREAMING_SNAKE_CASE						,		attention_head_dim=_SCREAMING_SNAKE_CASE						,		temb_channels=_SCREAMING_SNAKE_CASE						,		resnet_time_scale_shift=_SCREAMING_SNAKE_CASE						,		)
														self.up_blocks.append(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			output_channel

								# out
								if norm_type == "spatial":
														_UpperCAmelCase      =			SpatialNorm(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			nn.GroupNorm(num_channels=block_out_channels[0]						,		num_groups=_SCREAMING_SNAKE_CASE						,		eps=1e-6			)
								_UpperCAmelCase      =			nn.SiLU()
								_UpperCAmelCase      =			nn.Convad(block_out_channels[0]						,		_SCREAMING_SNAKE_CASE						,		3						,		padding=1			)

								_UpperCAmelCase      =			False






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None			):
								_UpperCAmelCase      =			z
								_UpperCAmelCase      =			self.conv_in(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			next(iter(self.up_blocks.parameters()			)			).dtype
								if self.training and self.gradient_checkpointing:

														def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																				def custom_forward(*_SCREAMING_SNAKE_CASE			):
																										return module(*_SCREAMING_SNAKE_CASE			)

																				return custom_forward

														if is_torch_version(""">="""						,		"""1.11.0"""			):
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		use_reentrant=_SCREAMING_SNAKE_CASE			)
														else:
																				# middle
																				_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(
																				    create_custom_forward(self.mid_block			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
																				_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

																				# up
																				for up_block in self.up_blocks:
																										_UpperCAmelCase      =			torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE			)						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								else:
														# middle
														_UpperCAmelCase      =			self.mid_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			sample.to(_SCREAMING_SNAKE_CASE			)

														# up
														for up_block in self.up_blocks:
																				_UpperCAmelCase      =			up_block(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

        # post-process
								if latent_embeds is None:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			self.conv_norm_out(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_act(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			self.conv_out(_SCREAMING_SNAKE_CASE			)

								return sample




class       _A				(		nn.Module     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE="random"						,		_SCREAMING_SNAKE_CASE=False						,		_SCREAMING_SNAKE_CASE=True			):
								super().__init__()
								_UpperCAmelCase      =			n_e
								_UpperCAmelCase      =			vq_embed_dim
								_UpperCAmelCase      =			beta
								_UpperCAmelCase      =			legacy

								_UpperCAmelCase      =			nn.Embedding(self.n_e						,		self.vq_embed_dim			)
								self.embedding.weight.data.uniform_(-1.0 / self.n_e						,		1.0 / self.n_e			)

								_UpperCAmelCase      =			remap
								if self.remap is not None:
														self.register_buffer("""used"""						,		torch.tensor(np.load(self.remap			)			)			)
														_UpperCAmelCase      =			self.used.shape[0]
														_UpperCAmelCase      =			unknown_index  # "random" or "extra" or integer
														if self.unknown_index == "extra":
																				_UpperCAmelCase      =			self.re_embed
																				_UpperCAmelCase      =			self.re_embed + 1
														print(
														    F"Remapping {self.n_e} indices to {self.re_embed} indices. "
														    F"Using {self.unknown_index} for unknown indices."			)
								else:
														_UpperCAmelCase      =			n_e

								_UpperCAmelCase      =			sane_index_shape






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			(inds[:, :, None] == used[None, None, ...]).long()
								_UpperCAmelCase      =			match.argmax(-1			)
								_UpperCAmelCase      =			match.sum(2			) < 1
								if self.unknown_index == "random":
														_UpperCAmelCase      =			torch.randint(0						,		self.re_embed						,		size=new[unknown].shape			).to(device=new.device			)
								else:
														_UpperCAmelCase      =			self.unknown_index
								return new.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			inds.shape
								assert len(_SCREAMING_SNAKE_CASE			) > 1
								_UpperCAmelCase      =			inds.reshape(ishape[0]						,		-1			)
								_UpperCAmelCase      =			self.used.to(_SCREAMING_SNAKE_CASE			)
								if self.re_embed > self.used.shape[0]:  # extra token
														_UpperCAmelCase      =			0  # simply set to zero
								_UpperCAmelCase      =			torch.gather(used[None, :][inds.shape[0] * [0], :]						,		1						,		_SCREAMING_SNAKE_CASE			)
								return back.reshape(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								# reshape z -> (batch, height, width, channel) and flatten
								_UpperCAmelCase      =			z.permute(0						,		2						,		3						,		1			).contiguous()
								_UpperCAmelCase      =			z.view(-1						,		self.vq_embed_dim			)

								# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
								_UpperCAmelCase      =			torch.argmin(torch.cdist(_SCREAMING_SNAKE_CASE						,		self.embedding.weight			)						,		dim=1			)

								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			).view(z.shape			)
								_UpperCAmelCase      =			None
								_UpperCAmelCase      =			None

								# compute loss for embedding
								if not self.legacy:
														_UpperCAmelCase      =			self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
								else:
														_UpperCAmelCase      =			torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

								# preserve gradients
								_UpperCAmelCase      =			z + (z_q - z).detach()

								# reshape back to match original input shape
								_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								if self.remap is not None:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z.shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.remap_to_used(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			min_encoding_indices.reshape(-1						,		1			)  # flatten

								if self.sane_index_shape:
														_UpperCAmelCase      =			min_encoding_indices.reshape(z_q.shape[0]						,		z_q.shape[2]						,		z_q.shape[3]			)

								return z_q, loss, (perplexity, min_encodings, min_encoding_indices)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
								# shape specifying (batch, height, width, channel)
								if self.remap is not None:
														_UpperCAmelCase      =			indices.reshape(shape[0]						,		-1			)  # add batch axis
														_UpperCAmelCase      =			self.unmap_to_all(_SCREAMING_SNAKE_CASE			)
														_UpperCAmelCase      =			indices.reshape(-1			)  # flatten again

								# get quantized latent vectors
								_UpperCAmelCase      =			self.embedding(_SCREAMING_SNAKE_CASE			)

								if shape is not None:
														_UpperCAmelCase      =			z_q.view(_SCREAMING_SNAKE_CASE			)
														# reshape back to match original input shape
														_UpperCAmelCase      =			z_q.permute(0						,		3						,		1						,		2			).contiguous()

								return z_q




class       _A				(		__lowercase     ):






		def __init__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=False			):
								_UpperCAmelCase      =			parameters
								_UpperCAmelCase    ,					_UpperCAmelCase      =			torch.chunk(_SCREAMING_SNAKE_CASE						,		2						,		dim=1			)
								_UpperCAmelCase      =			torch.clamp(self.logvar						,		-30.0						,		20.0			)
								_UpperCAmelCase      =			deterministic
								_UpperCAmelCase      =			torch.exp(0.5 * self.logvar			)
								_UpperCAmelCase      =			torch.exp(self.logvar			)
								if self.deterministic:
														_UpperCAmelCase      =			_UpperCAmelCase      =			torch.zeros_like(
														    self.mean						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = None			):
								# make sure sample is on the same device as the parameters and has same dtype
								_UpperCAmelCase      =			randn_tensor(
								    self.mean.shape						,		generator=_SCREAMING_SNAKE_CASE						,		device=self.parameters.device						,		dtype=self.parameters.dtype			)
								_UpperCAmelCase      =			self.mean + self.std * sample
								return x






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								else:
														if other is None:
																				return 0.5 * torch.sum(torch.pow(self.mean						,		2			) + self.var - 1.0 - self.logvar						,		dim=[1, 2, 3]			)
														else:
																				return 0.5 * torch.sum(
																				    torch.pow(self.mean - other.mean						,		2			) / other.var
																				    + self.var / other.var
																				    - 1.0
																				    - self.logvar
																				    + other.logvar						,		dim=[1, 2, 3]						,		)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=[1, 2, 3]			):
								if self.deterministic:
														return torch.Tensor([0.0]			)
								_UpperCAmelCase      =			np.log(2.0 * np.pi			)
								return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean						,		2			) / self.var						,		dim=_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								return self.mean