import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class  A_			:

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=sys.maxsize							):
   __a							=	"bilinear"
   __a							=	max_size
   __a							=	short_edge_length

  def __call__(		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	[]
   for img in imgs:
    __a							,  __a							=	img.shape[:2]
    # later: provide list and randomly choose index for resize
    __a							=	np.random.randint(self.short_edge_length[0]  ,				self.short_edge_length[1] + 1							)
    if size == 0:
     return img
    __a							=	size * 1.0 / min(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    if h < w:
     __a							,  __a							=	size, scale * w
    else:
     __a							,  __a							=	scale * h, size
    if max(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) > self.max_size:
     __a							=	self.max_size * 1.0 / max(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
     __a							=	newh * scale
     __a							=	neww * scale
    __a							=	int(neww + 0.5							)
    __a							=	int(newh + 0.5							)

    if img.dtype == np.uinta:
     __a							=	Image.fromarray(__SCREAMING_SNAKE_CASE							)
     __a							=	pil_image.resize((neww, newh)  ,				PILImageResampling.BILINEAR							)
     __a							=	np.asarray(__SCREAMING_SNAKE_CASE							)
    else:
     __a							=	img.permute(2  ,				0  ,				1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
     __a							=	nn.functional.interpolate(
         __SCREAMING_SNAKE_CASE  ,				(newh, neww)  ,				mode=self.interp_method  ,				align_corners=__SCREAMING_SNAKE_CASE							).squeeze(0							)
    img_augs.append(__SCREAMING_SNAKE_CASE							)

   return img_augs
class  A_			:

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   int							):
   __a							=	ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,				cfg.INPUT.MAX_SIZE_TEST							)
   __a							=	cfg.INPUT.FORMAT
   __a							=	cfg.SIZE_DIVISIBILITY
   __a							=	cfg.PAD_VALUE
   __a							=	cfg.INPUT.MAX_SIZE_TEST
   __a							=	cfg.MODEL.DEVICE
   __a							=	torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)  ,				1  ,				1							)
   __a							=	torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)  ,				1  ,				1							)
   __a							=	lambda __SCREAMING_SNAKE_CASE							: (x - self.pixel_mean) / self.pixel_std

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	tuple(max(__SCREAMING_SNAKE_CASE							) for s in zip(*[img.shape for img in images]							)							)
   __a							=	[im.shape[-2:] for im in images]
   __a							=	[
       nn.functional.pad(
           __SCREAMING_SNAKE_CASE  ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]  ,				value=self.pad_value  ,				)
       for size, im in zip(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   ]

   return torch.stack(__SCREAMING_SNAKE_CASE							), torch.tensor(__SCREAMING_SNAKE_CASE							)

  def __call__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple=False							):
   with torch.no_grad():
    if not isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							):
     __a							=	[images]
    if single_image:
     assert len(__SCREAMING_SNAKE_CASE							) == 1
    for i in range(len(__SCREAMING_SNAKE_CASE							)							):
     if isinstance(images[i]  ,				torch.Tensor							):
      images.insert(__SCREAMING_SNAKE_CASE  ,				images.pop(__SCREAMING_SNAKE_CASE							).to(self.device							).float()							)
     elif not isinstance(images[i]  ,				torch.Tensor							):
      images.insert(
          __SCREAMING_SNAKE_CASE  ,				torch.as_tensor(img_tensorize(images.pop(__SCREAMING_SNAKE_CASE							)  ,				input_format=self.input_format							)							)
          .to(self.device							)
          .float()  ,				)
            # resize smallest edge
    __a							=	torch.tensor([im.shape[:2] for im in images]							)
    __a							=	self.aug(__SCREAMING_SNAKE_CASE							)
    # transpose images and convert to torch tensors
    # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
    # now normalize before pad to avoid useless arithmetic
    __a							=	[self.normalizer(__SCREAMING_SNAKE_CASE							) for x in images]
    # now pad them to do the following operations
    __a							,  __a							=	self.pad(__SCREAMING_SNAKE_CASE							)
    # Normalize

    if self.size_divisibility > 0:
     raise NotImplementedError()
    # pad
    __a							=	torch.true_divide(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
    if single_image:
     return images[0], sizes[0], scales_yx[0]
    else:
     return images, sizes, scales_yx



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 boxes[:, 0::2] *= scale_yx[:, 1]
 boxes[:, 1::2] *= scale_yx[:, 0]
 return boxes






def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 assert torch.isfinite(_A       ).all(), "Box tensor contains infinite or NaN!"
 __a							,  __a							=	box_size
 tensor[:, 0].clamp_(min=0				,					max=_A       )
 tensor[:, 1].clamp_(min=0				,					max=_A       )
 tensor[:, 2].clamp_(min=0				,					max=_A       )
 tensor[:, 3].clamp_(min=0				,					max=_A       )



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							)


