

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block

@dataclass
class     __a   (      snake_case_    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						42



class     __a   (      snake_case_,    snake_case_    ):

			"""simple docstring"""

			@register_to_config
			def __init__(							self     :					int						,  lowercase_     :					List[Any] = None						,  lowercase_     :					int = 3						,  lowercase_     :					Union[str, Any] = 3						,  lowercase_     :					Any = False						,  lowercase_     :					Optional[Any] = "positional"						,  lowercase_     :					int = 0						,  lowercase_     :					Union[str, Any] = True						,  lowercase_     :					Optional[Any] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")						,  lowercase_     :					Dict = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")						,  lowercase_     :					Tuple = (224, 448, 672, 896)						,  lowercase_     :					List[str] = 2						,  lowercase_     :					Any = 1						,  lowercase_     :					List[str] = 1						,  lowercase_     :					int = "conv"						,  lowercase_     :					str = "conv"						,  lowercase_     :					Any = "silu"						,  lowercase_     :					Optional[Any] = 8						,  lowercase_     :					str = 32						,  lowercase_     :					Any = 1e-5						,  lowercase_     :					Tuple = "default"						,  lowercase_     :					Tuple = True						,  lowercase_     :					List[str] = None						,  lowercase_     :					List[Any] = None						,  ):
								super().__init__()

								UpperCamelCase__		:    Optional[int]         =sample_size
								UpperCamelCase__		:    Any         =block_out_channels[0] * 4

								# Check inputs
								if len(lowercase_						) != len(lowercase_						):
													raise ValueError(
													    f'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''						)

								if len(lowercase_						) != len(lowercase_						):
													raise ValueError(
													    f'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''						)

								# input
								UpperCamelCase__		:    List[str]         =nn.Convad(lowercase_						,  block_out_channels[0]						,  kernel_size=3						,  padding=(1, 1)						)

								# time
								if time_embedding_type == "fourier":
													UpperCamelCase__		:    Optional[Any]         =GaussianFourierProjection(embedding_size=block_out_channels[0]						,  scale=16						)
													UpperCamelCase__		:    Tuple         =2 * block_out_channels[0]
								elif time_embedding_type == "positional":
													UpperCamelCase__		:    Any         =Timesteps(block_out_channels[0]						,  lowercase_						,  lowercase_						)
													UpperCamelCase__		:    List[str]         =block_out_channels[0]

								UpperCamelCase__		:    Union[str, Any]         =TimestepEmbedding(lowercase_						,  lowercase_						)

								# class embedding
								if class_embed_type is None and num_class_embeds is not None:
													UpperCamelCase__		:    Optional[int]         =nn.Embedding(lowercase_						,  lowercase_						)
								elif class_embed_type == "timestep":
													UpperCamelCase__		:    Dict         =TimestepEmbedding(lowercase_						,  lowercase_						)
								elif class_embed_type == "identity":
													UpperCamelCase__		:    Optional[int]         =nn.Identity(lowercase_						,  lowercase_						)
								else:
													UpperCamelCase__		:    Tuple         =None

								UpperCamelCase__		:    Optional[Any]         =nn.ModuleList([]						)
								UpperCamelCase__		:    Tuple         =None
								UpperCamelCase__		:    str         =nn.ModuleList([]						)

								# down
								UpperCamelCase__		:    str         =block_out_channels[0]
								for i, down_block_type in enumerate(lowercase_						):
													UpperCamelCase__		:    Optional[Any]         =output_channel
													UpperCamelCase__		:    List[Any]         =block_out_channels[i]
													UpperCamelCase__		:    Optional[Any]         =i == len(lowercase_						) - 1

													UpperCamelCase__		:    Dict         =get_down_block(
													    lowercase_						,  num_layers=lowercase_						,  in_channels=lowercase_						,  out_channels=lowercase_						,  temb_channels=lowercase_						,  add_downsample=not is_final_block						,  resnet_eps=lowercase_						,  resnet_act_fn=lowercase_						,  resnet_groups=lowercase_						,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel						,  downsample_padding=lowercase_						,  resnet_time_scale_shift=lowercase_						,  downsample_type=lowercase_						,  )
													self.down_blocks.append(lowercase_						)

								# mid
								UpperCamelCase__		:    List[Any]         =UNetMidBlockaD(
								    in_channels=block_out_channels[-1]						,  temb_channels=lowercase_						,  resnet_eps=lowercase_						,  resnet_act_fn=lowercase_						,  output_scale_factor=lowercase_						,  resnet_time_scale_shift=lowercase_						,  attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]						,  resnet_groups=lowercase_						,  add_attention=lowercase_						,  )

								# up
								UpperCamelCase__		:    Optional[int]         =list(reversed(lowercase_						)						)
								UpperCamelCase__		:    int         =reversed_block_out_channels[0]
								for i, up_block_type in enumerate(lowercase_						):
													UpperCamelCase__		:    List[Any]         =output_channel
													UpperCamelCase__		:    Tuple         =reversed_block_out_channels[i]
													UpperCamelCase__		:    int         =reversed_block_out_channels[min(i + 1						,  len(lowercase_						) - 1						)]

													UpperCamelCase__		:    Optional[Any]         =i == len(lowercase_						) - 1

													UpperCamelCase__		:    Optional[Any]         =get_up_block(
													    lowercase_						,  num_layers=layers_per_block + 1						,  in_channels=lowercase_						,  out_channels=lowercase_						,  prev_output_channel=lowercase_						,  temb_channels=lowercase_						,  add_upsample=not is_final_block						,  resnet_eps=lowercase_						,  resnet_act_fn=lowercase_						,  resnet_groups=lowercase_						,  attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel						,  resnet_time_scale_shift=lowercase_						,  upsample_type=lowercase_						,  )
													self.up_blocks.append(lowercase_						)
													UpperCamelCase__		:    int         =output_channel

								# out
								UpperCamelCase__		:    List[Any]         =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4						,  32						)
								UpperCamelCase__		:    Union[str, Any]         =nn.GroupNorm(num_channels=block_out_channels[0]						,  num_groups=lowercase_						,  eps=lowercase_						)
								UpperCamelCase__		:    Union[str, Any]         =nn.SiLU()
								UpperCamelCase__		:    Dict         =nn.Convad(block_out_channels[0]						,  lowercase_						,  kernel_size=3						,  padding=1						)






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					List[Any]						,  lowercase_     :					int						,  lowercase_     :					Optional[int] = None						,  lowercase_     :					int = True						,  ):
								# 0. center input if necessary
								if self.config.center_input_sample:
													UpperCamelCase__		:    Optional[Any]         =2 * sample - 1.0

								# 1. time
								UpperCamelCase__		:    Any         =timestep
								if not torch.is_tensor(lowercase_						):
													UpperCamelCase__		:    List[Any]         =torch.tensor([timesteps]						,  dtype=torch.long						,  device=sample.device						)
								elif torch.is_tensor(lowercase_						) and len(timesteps.shape						) == 0:
													UpperCamelCase__		:    Optional[int]         =timesteps[None].to(sample.device						)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								UpperCamelCase__		:    Union[str, Any]         =timesteps * torch.ones(sample.shape[0]						,  dtype=timesteps.dtype						,  device=timesteps.device						)

								UpperCamelCase__		:    Union[str, Any]         =self.time_proj(lowercase_						)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might actually be running in fp16. so we need to cast here.
								# there might be better ways to encapsulate this.
								UpperCamelCase__		:    Union[str, Any]         =t_emb.to(dtype=self.dtype						)
								UpperCamelCase__		:    Dict         =self.time_embedding(lowercase_						)

								if self.class_embedding is not None:
													if class_labels is None:
																		raise ValueError('''class_labels should be provided when doing class conditioning'''						)

													if self.config.class_embed_type == "timestep":
																		UpperCamelCase__		:    Tuple         =self.time_proj(lowercase_						)

													UpperCamelCase__		:    Optional[int]         =self.class_embedding(lowercase_						).to(dtype=self.dtype						)
													UpperCamelCase__		:    Dict         =emb + class_emb

								# 2. pre-process
								UpperCamelCase__		:    List[Any]         =sample
								UpperCamelCase__		:    List[str]         =self.conv_in(lowercase_						)

								# 3. down
								UpperCamelCase__		:    Any         =(sample,)
								for downsample_block in self.down_blocks:
													if hasattr(lowercase_						,  '''skip_conv'''						):
																		UpperCamelCase__  ,			UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =downsample_block(
																		    hidden_states=lowercase_						,  temb=lowercase_						,  skip_sample=lowercase_						)
													else:
																		UpperCamelCase__  ,			UpperCamelCase__		:    Optional[int]         =downsample_block(hidden_states=lowercase_						,  temb=lowercase_						)

													down_block_res_samples += res_samples

								# 4. mid
								UpperCamelCase__		:    Optional[Any]         =self.mid_block(lowercase_						,  lowercase_						)

								# 5. up
								UpperCamelCase__		:    List[str]         =None
								for upsample_block in self.up_blocks:
													UpperCamelCase__		:    Union[str, Any]         =down_block_res_samples[-len(upsample_block.resnets						) :]
													UpperCamelCase__		:    Union[str, Any]         =down_block_res_samples[: -len(upsample_block.resnets						)]

													if hasattr(lowercase_						,  '''skip_conv'''						):
																		UpperCamelCase__  ,			UpperCamelCase__		:    Union[str, Any]         =upsample_block(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						)
													else:
																		UpperCamelCase__		:    Union[str, Any]         =upsample_block(lowercase_						,  lowercase_						,  lowercase_						)

        # 6. post-process
								UpperCamelCase__		:    int         =self.conv_norm_out(lowercase_						)
								UpperCamelCase__		:    Optional[Any]         =self.conv_act(lowercase_						)
								UpperCamelCase__		:    Optional[int]         =self.conv_out(lowercase_						)

								if skip_sample is not None:
													sample += skip_sample

								if self.config.time_embedding_type == "fourier":
													UpperCamelCase__		:    Dict         =timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]						)))						)
													UpperCamelCase__		:    Optional[int]         =sample / timesteps

								if not return_dict:
													return (sample,)

								return UNetaDOutput(sample=lowercase_						)





"""simple docstring"""





import io
import json
import subprocess


_SCREAMING_SNAKE_CASE       :  List[Any]        = [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_SCREAMING_SNAKE_CASE       :  str        = 8



def        _lowerCAmelCase      (						UpperCAmelCase     :							Tuple      ,							UpperCAmelCase     :							List[Any]				):


					'''simple docstring'''






					UpperCamelCase__		:    Any         ={}
					for src, tgt in pairs:
										UpperCamelCase__		:    Optional[int]         =F'''{src}-{tgt}'''
										UpperCamelCase__		:    int         =F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    List[Any]         =F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
										UpperCamelCase__		:    str         =subprocess.run(UpperCAmelCase      ,							stdout=subprocess.PIPE				).stdout.decode('''utf-8'''				).splitlines()
										UpperCamelCase__		:    Any         ={'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
					return text


_SCREAMING_SNAKE_CASE       :  List[str]        = get_all_data(pairs, n_objs)
_SCREAMING_SNAKE_CASE       :  Optional[Any]        = """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
				_SCREAMING_SNAKE_CASE       :  Any        = json.dump(text, f, indent=2, ensure_ascii=False)


