from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE			:						str				  =		"""
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

SCREAMING_SNAKE_CASE			:						int				  =		"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

SCREAMING_SNAKE_CASE			:						str				  =		"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A = None				,					_A = False				,					):



 """simple docstring"""


 if label_map is not None:
  for old_id, new_id in label_map.items():
   __a							=	new_id

    # turn into Numpy arrays
 __a							=	np.array(_A       )
 __a							=	np.array(_A       )

 if reduce_labels:
  __a							=	255
  __a							=	label - 1
  __a							=	255

 __a							=	label != ignore_index
 __a							=	np.not_equal(_A				,					_A       )
 __a							=	pred_label[mask]
 __a							=	np.array(_A       )[mask]

 __a							=	pred_label[pred_label == label]

 __a							=	np.histogram(_A				,					bins=_A				,					range=(0, num_labels - 1)       )[0]
 __a							=	np.histogram(_A				,					bins=_A				,					range=(0, num_labels - 1)       )[0]
 __a							=	np.histogram(_A				,					bins=_A				,					range=(0, num_labels - 1)       )[0]

 __a							=	area_pred_label + area_label - area_intersect

 return area_intersect, area_union, area_pred_label, area_label



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A = None				,					_A = False				,					):



 """simple docstring"""


 __a							=	np.zeros((num_labels,)				,					dtype=np.floataa       )
 __a							=	np.zeros((num_labels,)				,					dtype=np.floataa       )
 __a							=	np.zeros((num_labels,)				,					dtype=np.floataa       )
 __a							=	np.zeros((num_labels,)				,					dtype=np.floataa       )
 for result, gt_seg_map in zip(_A				,					_A       ):
  __a							,  __a							,  __a							,  __a							=	intersect_and_union(
      _A				,					_A				,					_A				,					_A				,					_A				,					_A       )
  total_area_intersect += area_intersect
  total_area_union += area_union
  total_area_pred_label += area_pred_label
  total_area_label += area_label
 return total_area_intersect, total_area_union, total_area_pred_label, total_area_label



def 	__A		(      _A				,					_A				,					_A				,					_A				,					_A = None				,					_A = None				,					_A = False				,					):



 """simple docstring"""


 __a							,  __a							,  __a							,  __a							=	total_intersect_and_union(
     _A				,					_A				,					_A				,					_A				,					_A				,					_A       )

 # compute metrics
 __a							=	{}

 __a							=	total_area_intersect.sum() / total_area_label.sum()
 __a							=	total_area_intersect / total_area_union
 __a							=	total_area_intersect / total_area_label

 __a							=	np.nanmean(_A       )
 __a							=	np.nanmean(_A       )
 __a							=	all_acc
 __a							=	iou
 __a							=	acc

 if nan_to_num is not None:
  __a							=	{metric: np.nan_to_num(_A				,					nan=_A       ) for metric, metric_value in metrics.items()}

 return metrics






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,							_KWARGS_DESCRIPTION			)
class  A_			(							datasets.Metric			):

  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   return datasets.MetricInfo(
       description=_DESCRIPTION  ,				citation=_CITATION  ,				inputs_description=_KWARGS_DESCRIPTION  ,				features=datasets.Features(
           # 1st Seq - height dim, 2nd - width dim
           {
               "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"							)							)							),
               "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16"							)							)							),
           }							)  ,				reference_urls=[
           "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
       ]  ,				)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   bool  ,				__SCREAMING_SNAKE_CASE			:   Optional[int] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[int, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = False  ,				):
   __a							=	mean_iou(
       results=__SCREAMING_SNAKE_CASE  ,				gt_seg_maps=__SCREAMING_SNAKE_CASE  ,				num_labels=__SCREAMING_SNAKE_CASE  ,				ignore_index=__SCREAMING_SNAKE_CASE  ,				nan_to_num=__SCREAMING_SNAKE_CASE  ,				label_map=__SCREAMING_SNAKE_CASE  ,				reduce_labels=__SCREAMING_SNAKE_CASE  ,				)
   return iou_result



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							)


