






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 (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      42





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase=3       ,    UpperCAmelCase=3       ,    UpperCAmelCase=("DownEncoderBlock2D",)       ,    UpperCAmelCase=(64,)       ,    UpperCAmelCase=2       ,    UpperCAmelCase=32       ,    UpperCAmelCase="silu"       ,    UpperCAmelCase=True       ,    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			layers_per_block

							_UpperCAmelCase									=			torch.nn.Convad(
							    UpperCAmelCase       ,    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(UpperCAmelCase    ):
													_UpperCAmelCase									=			output_channel
													_UpperCAmelCase									=			block_out_channels[i]
													_UpperCAmelCase									=			i == len(UpperCAmelCase    ) - 1

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

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

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

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

							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			x
							_UpperCAmelCase									=			self.conv_in(UpperCAmelCase    )

							if self.training and self.gradient_checkpointing:

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

																			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(UpperCAmelCase    )       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block    )       ,    UpperCAmelCase       ,    use_reentrant=UpperCAmelCase    )
													else:
																			for down_block in self.down_blocks:
																									_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase    )       ,    UpperCAmelCase    )
																			# middle
																			_UpperCAmelCase									=			torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block    )       ,    UpperCAmelCase    )

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

													# middle
													_UpperCAmelCase									=			self.mid_block(UpperCAmelCase    )

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

							return sample





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase=3       ,    UpperCAmelCase=3       ,    UpperCAmelCase=("UpDecoderBlock2D",)       ,    UpperCAmelCase=(64,)       ,    UpperCAmelCase=2       ,    UpperCAmelCase=32       ,    UpperCAmelCase="silu"       ,    UpperCAmelCase="group"       ,    ):
							"""simple docstring"""







							super().__init__()
							_UpperCAmelCase									=			layers_per_block

							_UpperCAmelCase									=			nn.Convad(
							    UpperCAmelCase       ,    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=UpperCAmelCase       ,    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=UpperCAmelCase       ,    temb_channels=UpperCAmelCase       ,    )

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

													_UpperCAmelCase									=			i == len(UpperCAmelCase    ) - 1

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

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

							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			z
							_UpperCAmelCase									=			self.conv_in(UpperCAmelCase    )

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

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

																			return custom_forward

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

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

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

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

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

							return sample





class __lowerCamelCase (      nn.Module):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase=None       ,    UpperCAmelCase="random"       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True    ):
							"""simple docstring"""







							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       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			inds.shape
							assert len(UpperCAmelCase    ) > 1
							_UpperCAmelCase									=			inds.reshape(ishape[0]       ,    -1    )
							_UpperCAmelCase									=			self.used.to(UpperCAmelCase    )
							_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(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			inds.shape
							assert len(UpperCAmelCase    ) > 1
							_UpperCAmelCase									=			inds.reshape(ishape[0]       ,    -1    )
							_UpperCAmelCase									=			self.used.to(UpperCAmelCase    )
							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       ,    UpperCAmelCase    )
							return back.reshape(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_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(UpperCAmelCase       ,    self.embedding.weight    )       ,    dim=1    )

							_UpperCAmelCase									=			self.embedding(UpperCAmelCase    ).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(UpperCAmelCase    )
													_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       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

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

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

							return z_q





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parameters
							_UpperCAmelCase					, _UpperCAmelCase									=			torch.chunk(UpperCAmelCase       ,    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       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			randn_tensor(
							    self.mean.shape       ,    generator=UpperCAmelCase       ,    device=self.parameters.device       ,    dtype=self.parameters.dtype    )
							_UpperCAmelCase									=			self.mean + self.std * sample
							return x

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							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       ,    UpperCAmelCase       ,    UpperCAmelCase=[1, 2, 3]    ):
							"""simple docstring"""







							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=UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self.mean






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 lowerCamelCase				:		List[Any]     =   None

lowerCamelCase				:		int     =   logging.get_logger(__name__)
lowerCamelCase				:		Optional[int]     =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase				:		Optional[int]     =   {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

lowerCamelCase				:		List[Any]     =   {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowerCamelCase				:		Any     =   "▁"







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						VOCAB_FILES_NAMES
 UpperCamelCase						   =						PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase						   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCamelCase						   =						['''input_ids''', '''token_type_ids''']
 UpperCamelCase						   =						FNetTokenizer

 def __init__(				self					:  Optional[int]						,							A_					:  Any=None						,							A_					:  int=None						,							A_					:  int=False						,							A_					:  Optional[int]=True						,							A_					:  List[Any]=True						,							A_					:  Tuple="<unk>"						,							A_					:  Optional[int]="[SEP]"						,							A_					:  List[Any]="<pad>"						,							A_					:  Optional[int]="[CLS]"						,							A_					:  Optional[Any]="[MASK]"						,							**A_					:  Dict						,							)   ->	List[str]:

       """simple docstring"""






       lowerCamelCase_		     =    (
           AddedToken(A_						,							lstrip=A_						,							rstrip=A_						,							normalized=A_  )
           if isinstance(A_						,							A_  )
           else mask_token
       )

       super().__init__(
           A_						,							tokenizer_file=A_						,							do_lower_case=A_						,							remove_space=A_						,							keep_accents=A_						,							unk_token=A_						,							sep_token=A_						,							pad_token=A_						,							cls_token=A_						,							mask_token=A_						,							**A_						,							)

       lowerCamelCase_		     =    do_lower_case
       lowerCamelCase_		     =    remove_space
       lowerCamelCase_		     =    keep_accents
       lowerCamelCase_		     =    vocab_file
       lowerCamelCase_		     =    False if not self.vocab_file else True

 def 				a__ (				self					:  List[Any]						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

 def 				a__ (				self					:  Tuple						,							A_					:  List[int]						,							A_					:  Optional[List[int]] = None  )   ->	List[int]:

       """simple docstring"""






       lowerCamelCase_		     =    [self.sep_token_id]
       lowerCamelCase_		     =    [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]





 def 				a__ (				self					:  Dict						,							A_					:  str						,							A_					:  Optional[str] = None  )   ->	Tuple[str]:

       """simple docstring"""






       if not os.path.isdir(A_  ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
             return
       lowerCamelCase_		     =    os.path.join(
           A_						,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

       if os.path.abspath(self.vocab_file  ) != os.path.abspath(A_  ):
             copyfile(self.vocab_file						,							A_  )

       return (out_vocab_file,)



