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 get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      42
class  A_			(							a_      ,							a_			):

  @register_to_config
  def __init__(		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   int = 6_55_36  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				__SCREAMING_SNAKE_CASE			:   int = 2  ,				__SCREAMING_SNAKE_CASE			:   int = 2  ,				__SCREAMING_SNAKE_CASE			:   int = 0  ,				__SCREAMING_SNAKE_CASE			:   str = "fourier"  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				__SCREAMING_SNAKE_CASE			:   float = 0.0  ,				__SCREAMING_SNAKE_CASE			:   Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")  ,				__SCREAMING_SNAKE_CASE			:   Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")  ,				__SCREAMING_SNAKE_CASE			:   Tuple[str] = "UNetMidBlock1D"  ,				__SCREAMING_SNAKE_CASE			:   str = None  ,				__SCREAMING_SNAKE_CASE			:   Tuple[int] = (32, 32, 64)  ,				__SCREAMING_SNAKE_CASE			:   str = None  ,				__SCREAMING_SNAKE_CASE			:   int = 8  ,				__SCREAMING_SNAKE_CASE			:   int = 1  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				):
   super().__init__()
   __a							=	sample_size

   # time
   if time_embedding_type == "fourier":
    __a							=	GaussianFourierProjection(
        embedding_size=8  ,				set_W_to_weight=__SCREAMING_SNAKE_CASE  ,				log=__SCREAMING_SNAKE_CASE  ,				flip_sin_to_cos=__SCREAMING_SNAKE_CASE							)
    __a							=	2 * block_out_channels[0]
   elif time_embedding_type == "positional":
    __a							=	Timesteps(
        block_out_channels[0]  ,				flip_sin_to_cos=__SCREAMING_SNAKE_CASE  ,				downscale_freq_shift=__SCREAMING_SNAKE_CASE							)
    __a							=	block_out_channels[0]

   if use_timestep_embedding:
    __a							=	block_out_channels[0] * 4
    __a							=	TimestepEmbedding(
        in_channels=__SCREAMING_SNAKE_CASE  ,				time_embed_dim=__SCREAMING_SNAKE_CASE  ,				act_fn=__SCREAMING_SNAKE_CASE  ,				out_dim=block_out_channels[0]  ,				)

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

   # down
   __a							=	in_channels
   for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE							):
    __a							=	output_channel
    __a							=	block_out_channels[i]

    if i == 0:
     input_channel += extra_in_channels

    __a							=	i == len(__SCREAMING_SNAKE_CASE							) - 1

    __a							=	get_down_block(
        __SCREAMING_SNAKE_CASE  ,				num_layers=__SCREAMING_SNAKE_CASE  ,				in_channels=__SCREAMING_SNAKE_CASE  ,				out_channels=__SCREAMING_SNAKE_CASE  ,				temb_channels=block_out_channels[0]  ,				add_downsample=not is_final_block or downsample_each_block  ,				)
    self.down_blocks.append(__SCREAMING_SNAKE_CASE							)

   # mid
   __a							=	get_mid_block(
       __SCREAMING_SNAKE_CASE  ,				in_channels=block_out_channels[-1]  ,				mid_channels=block_out_channels[-1]  ,				out_channels=block_out_channels[-1]  ,				embed_dim=block_out_channels[0]  ,				num_layers=__SCREAMING_SNAKE_CASE  ,				add_downsample=__SCREAMING_SNAKE_CASE  ,				)

   # up
   __a							=	list(reversed(__SCREAMING_SNAKE_CASE							)							)
   __a							=	reversed_block_out_channels[0]
   if out_block_type is None:
    __a							=	out_channels
   else:
    __a							=	block_out_channels[0]

   for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE							):
    __a							=	output_channel
    __a							=	(
        reversed_block_out_channels[i + 1] if i < len(__SCREAMING_SNAKE_CASE							) - 1 else final_upsample_channels
    )

    __a							=	i == len(__SCREAMING_SNAKE_CASE							) - 1

    __a							=	get_up_block(
        __SCREAMING_SNAKE_CASE  ,				num_layers=__SCREAMING_SNAKE_CASE  ,				in_channels=__SCREAMING_SNAKE_CASE  ,				out_channels=__SCREAMING_SNAKE_CASE  ,				temb_channels=block_out_channels[0]  ,				add_upsample=not is_final_block  ,				)
    self.up_blocks.append(__SCREAMING_SNAKE_CASE							)
    __a							=	output_channel

   # out
   __a							=	norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,				32							)
   __a							=	get_out_block(
       out_block_type=__SCREAMING_SNAKE_CASE  ,				num_groups_out=__SCREAMING_SNAKE_CASE  ,				embed_dim=block_out_channels[0]  ,				out_channels=__SCREAMING_SNAKE_CASE  ,				act_fn=__SCREAMING_SNAKE_CASE  ,				fc_dim=block_out_channels[-1] // 4  ,				)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   torch.FloatTensor  ,				__SCREAMING_SNAKE_CASE			:   Union[torch.Tensor, float, int]  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				):
   __a							=	timestep
   if not torch.is_tensor(__SCREAMING_SNAKE_CASE							):
    __a							=	torch.tensor([timesteps]  ,				dtype=torch.long  ,				device=sample.device							)
   elif torch.is_tensor(__SCREAMING_SNAKE_CASE							) and len(timesteps.shape							) == 0:
    __a							=	timesteps[None].to(sample.device							)

   __a							=	self.time_proj(__SCREAMING_SNAKE_CASE							)
   if self.config.use_timestep_embedding:
    __a							=	self.time_mlp(__SCREAMING_SNAKE_CASE							)
   else:
    __a							=	timestep_embed[..., None]
    __a							=	timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
    __a							=	timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

   # 2. down
   __a							=	()
   for downsample_block in self.down_blocks:
    __a							,  __a							=	downsample_block(hidden_states=__SCREAMING_SNAKE_CASE  ,				temb=__SCREAMING_SNAKE_CASE							)
    down_block_res_samples += res_samples

   # 3. mid
   if self.mid_block:
    __a							=	self.mid_block(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   # 4. up
   for i, upsample_block in enumerate(self.up_blocks							):
    __a							=	down_block_res_samples[-1:]
    __a							=	down_block_res_samples[:-1]
    __a							=	upsample_block(__SCREAMING_SNAKE_CASE  ,				res_hidden_states_tuple=__SCREAMING_SNAKE_CASE  ,				temb=__SCREAMING_SNAKE_CASE							)

   # 5. post-process
   if self.out_block:
    __a							=	self.out_block(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)

   if not return_dict:
    return (sample,)

   return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE							)



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


