




"""simple docstring"""







from typing import Dict, Optional

import numpy as np

import datasets


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

a     :   Any      =							'\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'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.        ])}\n'

a     :   Any      =							'\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'





def 							lowercase					(snake_case__   : Union[str, Any]      ,       snake_case__   : Tuple      ,       snake_case__   : Union[str, Any]      ,       snake_case__   : Dict      ,       snake_case__   : List[Any] = None      ,       snake_case__   : Dict = False      ,       )     ->  List[str]:


				'''simple docstring'''






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

    # turn into Numpy arrays
				lowerCAmelCase  =				np.array(lowerCamelCase__ )
				lowerCAmelCase  =				np.array(lowerCamelCase__ )

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

				lowerCAmelCase  =				label != ignore_index
				lowerCAmelCase  =				np.not_equal(lowerCamelCase__      ,       lowerCamelCase__ )
				lowerCAmelCase  =				pred_label[mask]
				lowerCAmelCase  =				np.array(lowerCamelCase__ )[mask]

				lowerCAmelCase  =				pred_label[pred_label == label]

				lowerCAmelCase  =				np.histogram(lowerCamelCase__      ,       bins=lowerCamelCase__      ,       range=(0, num_labels - 1) )[0]
				lowerCAmelCase  =				np.histogram(lowerCamelCase__      ,       bins=lowerCamelCase__      ,       range=(0, num_labels - 1) )[0]
				lowerCAmelCase  =				np.histogram(lowerCamelCase__      ,       bins=lowerCamelCase__      ,       range=(0, num_labels - 1) )[0]

				lowerCAmelCase  =				area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label





def 							lowercase					(snake_case__   : str      ,       snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : int      ,       snake_case__   : Union[str, Any] = None      ,       snake_case__   : Optional[Any] = False      ,       )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase  =				np.zeros((num_labels,)      ,       dtype=np.floataa )
				lowerCAmelCase  =				np.zeros((num_labels,)      ,       dtype=np.floataa )
				lowerCAmelCase  =				np.zeros((num_labels,)      ,       dtype=np.floataa )
				lowerCAmelCase  =				np.zeros((num_labels,)      ,       dtype=np.floataa )
				for result, gt_seg_map in zip(lowerCamelCase__      ,       lowerCamelCase__ ):
								lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				intersect_and_union(
								    lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__ )
								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 							lowercase					(snake_case__   : Optional[int]      ,       snake_case__   : Any      ,       snake_case__   : Optional[int]      ,       snake_case__   : Any      ,       snake_case__   : Any = None      ,       snake_case__   : Optional[Any] = None      ,       snake_case__   : List[Any] = False      ,       )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				total_intersect_and_union(
				    lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__      ,       lowerCamelCase__ )

				# compute metrics
				lowerCAmelCase  =				{}

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

				lowerCAmelCase  =				np.nanmean(lowerCamelCase__ )
				lowerCAmelCase  =				np.nanmean(lowerCamelCase__ )
				lowerCAmelCase  =				all_acc
				lowerCAmelCase  =				iou
				lowerCAmelCase  =				acc

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

				return metrics






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


						def   __lowercase (							self				:			Tuple					):
										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   __lowercase (							self				:			str						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			bool						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[Dict[int, int]] = None						,						lowerCAmelCase				:			bool = False						,						):
										lowerCAmelCase  =				mean_iou(
										    results=lowerCAmelCase						,						gt_seg_maps=lowerCAmelCase						,						num_labels=lowerCAmelCase						,						ignore_index=lowerCAmelCase						,						nan_to_num=lowerCAmelCase						,						label_map=lowerCAmelCase						,						reduce_labels=lowerCAmelCase						,						)
										return iou_result











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

				lowerCAmelCase  =				{key.replace("""decoder"""      ,       """model""" ): val for key, val in state_dict.items()}

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       max_position_embeddings=args.max_target_positions      ,       num_layers=args.decoder_layers      ,       attention_heads=args.decoder_attention_heads      ,       ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.decoder_embed_dim      ,       layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="""gelu"""      ,       scale_embedding=not args.no_scale_embedding      ,       tie_word_embeddings=args.share_decoder_input_output_embed      ,       )

				lowerCAmelCase  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





