
"""simple docstring"""


from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching






def       _snake_case				(		):
	import os as original_os
	from os import path as original_path
	from os import rename as original_rename
	from os.path import dirname as original_dirname
	from os.path import join as original_join

	assert _test_patching.os is original_os
	assert _test_patching.path is original_path
	assert _test_patching.join is original_join

	assert _test_patching.renamed_os is original_os
	assert _test_patching.renamed_path is original_path
	assert _test_patching.renamed_join is original_join

	A						   =   '__test_patch_submodule_mock__'
	with patch_submodule(_test_patching	,      'os.path.join'	,      snake_case__     ):
		# Every way to access os.path.join must be patched, and the rest must stay untouched

		# check os.path.join
		assert isinstance(_test_patching.os	,      _PatchedModuleObj     )
		assert isinstance(_test_patching.os.path	,      _PatchedModuleObj     )
		assert _test_patching.os.path.join is mock

		# check path.join
		assert isinstance(_test_patching.path	,      _PatchedModuleObj     )
		assert _test_patching.path.join is mock

		# check join
		assert _test_patching.join is mock

		# check that the other attributes are untouched
		assert _test_patching.os.rename is original_rename
		assert _test_patching.path.dirname is original_dirname
		assert _test_patching.os.path.dirname is original_dirname

		# Even renamed modules or objects must be patched

		# check renamed_os.path.join
		assert isinstance(_test_patching.renamed_os	,      _PatchedModuleObj     )
		assert isinstance(_test_patching.renamed_os.path	,      _PatchedModuleObj     )
		assert _test_patching.renamed_os.path.join is mock

		# check renamed_path.join
		assert isinstance(_test_patching.renamed_path	,      _PatchedModuleObj     )
		assert _test_patching.renamed_path.join is mock

		# check renamed_join
		assert _test_patching.renamed_join is mock

		# check that the other attributes are untouched
		assert _test_patching.renamed_os.rename is original_rename
		assert _test_patching.renamed_path.dirname is original_dirname
		assert _test_patching.renamed_os.path.dirname is original_dirname

	# check that everthing is back to normal when the patch is over

	assert _test_patching.os is original_os
	assert _test_patching.path is original_path
	assert _test_patching.join is original_join

	assert _test_patching.renamed_os is original_os
	assert _test_patching.renamed_path is original_path
	assert _test_patching.renamed_join is original_join






def       _snake_case				(		):
	assert _test_patching.open is open

	A						   =   '__test_patch_submodule_builtin_mock__'
	# _test_patching has "open" in its globals
	assert _test_patching.open is open
	with patch_submodule(_test_patching	,      'open'	,      snake_case__     ):
		assert _test_patching.open is mock

	# check that everthing is back to normal when the patch is over

	assert _test_patching.open is open






def       _snake_case				(		):
	# pandas.read_csv is not present in _test_patching
	A						   =   '__test_patch_submodule_missing_mock__'
	with patch_submodule(_test_patching	,      'pandas.read_csv'	,      snake_case__     ):
		pass






def       _snake_case				(		):
	# builtin should always be mocked even if they're not in the globals
	# in case they're loaded at one point
	A						   =   '__test_patch_submodule_missing_builtin_mock__'
	# _test_patching doesn't have "len" in its globals
	assert getattr(_test_patching	,      'len'	,      snake_case__     ) is None
	with patch_submodule(_test_patching	,      'len'	,      snake_case__     ):
		assert _test_patching.len is mock
	assert _test_patching.len is len






def       _snake_case				(		):
	A						   =   '__test_patch_submodule_start_and_stop_mock__'
	A						   =   patch_submodule(_test_patching	,      'open'	,      snake_case__     )
	assert _test_patching.open is open
	patch.start()
	assert _test_patching.open is mock
	patch.stop()
	assert _test_patching.open is open






def       _snake_case				(		):
	from os import rename as original_rename
	from os.path import dirname as original_dirname
	from os.path import join as original_join

	A						   =   '__test_patch_submodule_successive_join__'
	A						   =   '__test_patch_submodule_successive_dirname__'
	A						   =   '__test_patch_submodule_successive_rename__'
	assert _test_patching.os.path.join is original_join
	assert _test_patching.os.path.dirname is original_dirname
	assert _test_patching.os.rename is original_rename

	with patch_submodule(_test_patching	,      'os.path.join'	,      snake_case__     ):
		with patch_submodule(_test_patching	,      'os.rename'	,      snake_case__     ):
			with patch_submodule(_test_patching	,      'os.path.dirname'	,      snake_case__     ):
				assert _test_patching.os.path.join is mock_join
				assert _test_patching.os.path.dirname is mock_dirname
				assert _test_patching.os.rename is mock_rename

    # try another order
	with patch_submodule(_test_patching	,      'os.rename'	,      snake_case__     ):
		with patch_submodule(_test_patching	,      'os.path.join'	,      snake_case__     ):
			with patch_submodule(_test_patching	,      'os.path.dirname'	,      snake_case__     ):
				assert _test_patching.os.path.join is mock_join
				assert _test_patching.os.path.dirname is mock_dirname
				assert _test_patching.os.rename is mock_rename

	assert _test_patching.os.path.join is original_join
	assert _test_patching.os.path.dirname is original_dirname
	assert _test_patching.os.rename is original_rename






def       _snake_case				(		):
	A						   =   '__test_patch_submodule_doesnt_exist_mock__'
	with patch_submodule(_test_patching	,      '__module_that_doesn_exist__.__attribute_that_doesn_exist__'	,      snake_case__     ):
		pass
	with patch_submodule(_test_patching	,      'os.__attribute_that_doesn_exist__'	,      snake_case__     ):
		pass

"""simple docstring"""


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 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    torch.FloatTensor







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[str]  ,A_     :      Dict=3  ,A_     :      int=3  ,A_     :      str=("DownEncoderBlock2D",)  ,A_     :      Dict=(64,)  ,A_     :      str=2  ,A_     :      Union[str, Any]=32  ,A_     :      Optional[int]="silu"  ,A_     :      str=True  ,)     ->   Union[str, Any]:
							super().__init__()
							A						   =   layers_per_block

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

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

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

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

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,output_scale_factor=1  ,resnet_time_scale_shift='default'  ,attention_head_dim=block_out_channels[-1]  ,resnet_groups=A_  ,temb_channels=A_  ,)

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

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

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   x
							A						   =   self.conv_in(A_      )

							if self.training and self.gradient_checkpointing:

								def create_custom_forward(A_     :      Dict      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

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

							else:
								# down
								for down_block in self.down_blocks:
									A						   =   down_block(A_      )

								# middle
								A						   =   self.mid_block(A_      )

							# post-process
							A						   =   self.conv_norm_out(A_      )
							A						   =   self.conv_act(A_      )
							A						   =   self.conv_out(A_      )

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]=3  ,A_     :      Optional[int]=3  ,A_     :      str=("UpDecoderBlock2D",)  ,A_     :      Any=(64,)  ,A_     :      Optional[int]=2  ,A_     :      Optional[int]=32  ,A_     :      Tuple="silu"  ,A_     :      Optional[int]="group"  ,)     ->   Any:
							super().__init__()
							A						   =   layers_per_block

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

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

							A						   =   in_channels if norm_type == 'spatial' else None

							# mid
							A						   =   UNetMidBlockaD(
							    in_channels=block_out_channels[-1]  ,resnet_eps=1e-6  ,resnet_act_fn=A_  ,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=A_  ,temb_channels=A_  ,)

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

								A						   =   i == len(A_      ) - 1

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

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

							A						   =   False




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      str  ,A_     :      Union[str, Any]=None      )     ->   Any:
							A						   =   z
							A						   =   self.conv_in(A_      )

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

								def create_custom_forward(A_     :      List[Any]      ):
									def custom_forward(*A_     :      Tuple      ):
										return module(*A_      )

									return custom_forward

								if is_torch_version('>='  ,'1.11.0'      ):
									# middle
									A						   =   torch.utils.checkpoint.checkpoint(
									    create_custom_forward(self.mid_block      )  ,A_  ,A_  ,use_reentrant=A_      )
									A						   =   sample.to(A_      )

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

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

								# up
								for up_block in self.up_blocks:
									A						   =   up_block(A_  ,A_      )

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

							return sample







class 				lowerCAmelCase_				(      nn.Module    ):



						'''simple docstring'''


						def __init__( self     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      str  ,A_     :      Dict=None  ,A_     :      List[Any]="random"  ,A_     :      Optional[int]=False  ,A_     :      str=True      )     ->   List[str]:
							super().__init__()
							A						   =   n_e
							A						   =   vq_embed_dim
							A						   =   beta
							A						   =   legacy

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

							A						   =   remap
							if self.remap is not None:
								self.register_buffer('used'  ,torch.tensor(np.load(self.remap      )      )      )
								A						   =   self.used.shape[0]
								A						   =   unknown_index  # "random" or "extra" or integer
								if self.unknown_index == "extra":
									A						   =   self.re_embed
									A						   =   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:
								A						   =   n_e

							A						   =   sane_index_shape


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


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      List[Any]      )     ->   str:
							# reshape z -> (batch, height, width, channel) and flatten
							A						   =   z.permute(0  ,2  ,3  ,1      ).contiguous()
							A						   =   z.view(-1  ,self.vq_embed_dim      )

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

							A						   =   self.embedding(A_      ).view(z.shape      )
							A						   =   None
							A						   =   None

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

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

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

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

							if self.sane_index_shape:
								A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      str      )     ->   Union[str, Any]:
							# shape specifying (batch, height, width, channel)
							if self.remap is not None:
								A						   =   indices.reshape(shape[0]  ,-1      )  # add batch axis
								A						   =   self.unmap_to_all(A_      )
								A						   =   indices.reshape(-1      )  # flatten again

							# get quantized latent vectors
							A						   =   self.embedding(A_      )

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

							return z_q







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      Tuple  ,A_     :      Dict=False      )     ->   List[str]:
							A						   =   parameters
							A   ,      A						   =   torch.chunk(A_  ,2  ,dim=1      )
							A						   =   torch.clamp(self.logvar  ,-30.0  ,20.0      )
							A						   =   deterministic
							A						   =   torch.exp(0.5 * self.logvar      )
							A						   =   torch.exp(self.logvar      )
							if self.deterministic:
								A						   =   A						   =   torch.zeros_like(
								    self.mean  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Optional[torch.Generator] = None      )     ->   torch.FloatTensor:
							# make sure sample is on the same device as the parameters and has same dtype
							A						   =   randn_tensor(
							    self.mean.shape  ,generator=A_  ,device=self.parameters.device  ,dtype=self.parameters.dtype      )
							A						   =   self.mean + self.std * sample
							return x


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      Tuple=None      )     ->   int:
							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 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]  ,A_     :      Union[str, Any]=[1, 2, 3]      )     ->   List[str]:
							if self.deterministic:
								return torch.Tensor([0.0]      )
							A						   =   np.log(2.0 * np.pi      )
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean  ,2      ) / self.var  ,dim=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[int]:
							return self.mean