






'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


A:      Optional[Any]  				=				logging.get_logger(__name__)
logging.set_verbosity_info()






def 	_UpperCAmelCase    (  a		:	Union[str, Any]						,			a		:	int							)			->			List[Any]:





						"""simple docstring"""






						if "xprophetnet" in prophetnet_checkpoint_path:
												lowercase_      :   Dict        =			XLMProphetNetForConditionalGenerationOld.from_pretrained(a							)
												lowercase_      :   List[str]        =			XLMProphetNetForConditionalGeneration.from_pretrained(
												    a						,			output_loading_info=a							)
						else:
												lowercase_      :   List[Any]        =			ProphetNetForConditionalGenerationOld.from_pretrained(a							)
												lowercase_      :   List[str]        =			ProphetNetForConditionalGeneration.from_pretrained(
												    a						,			output_loading_info=a							)

						lowercase_      :   Tuple        =			['''key_proj''', '''value_proj''', '''query_proj''']

						lowercase_      :   List[Any]        =			{
						    '''self_attn''': '''ngram_self_attn''',
						    '''cross_attn''': '''encoder_attn''',
						    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
						    '''feed_forward_layer_norm''': '''final_layer_norm''',
						    '''feed_forward''': '''''',
						    '''intermediate''': '''fc1''',
						    '''output''': '''fc2''',
						    '''key_proj''': '''k_proj''',
						    '''query_proj''': '''q_proj''',
						    '''value_proj''': '''v_proj''',
						    '''word_embeddings''': '''embed_tokens''',
						    '''embeddings_layer_norm''': '''emb_layer_norm''',
						    '''relative_pos_embeddings''': '''relative_linear''',
						    '''ngram_embeddings''': '''ngram_input_embed''',
						    '''position_embeddings''': '''embed_positions''',
						}

						for key in loading_info["missing_keys"]:
												lowercase_      :   Optional[int]        =			key.split('.'							)

												if attributes[0] == "lm_head":
																		lowercase_      :   Optional[Any]        =			prophet
																		lowercase_      :   int        =			prophet_old
												else:
																		lowercase_      :   List[str]        =			prophet.prophetnet
																		lowercase_      :   str        =			prophet_old.model

												lowercase_      :   Any        =			False
												for attribute in attributes:
																		if attribute in mapping:
																								lowercase_      :   Optional[Any]        =			mapping[attribute]
																								if not hasattr(a						,			a							) and len(a							) > 0:
																														lowercase_      :   Union[str, Any]        =			attribute
																		elif hasattr(a						,			a							):
																								lowercase_      :   Optional[int]        =			attribute

																		if attribute == "weight":
																								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																								lowercase_      :   str        =			old_model.weight
																								logger.info(f"{attribute} is initialized."							)
																								lowercase_      :   List[Any]        =			True
																								break
																		elif attribute == "bias":
																								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																								lowercase_      :   Union[str, Any]        =			old_model.bias
																								logger.info(f"{attribute} is initialized"							)
																								lowercase_      :   Dict        =			True
																								break
																		elif attribute in special_keys and hasattr(a						,			'in_proj_weight'							):
																								lowercase_      :   List[Any]        =			old_model.in_proj_weight.shape[0] // 3
																								lowercase_      :   Optional[int]        =			getattr(a						,			a							)
																								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																								if attribute == "query_proj":
																														lowercase_      :   str        =			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]							)
																														lowercase_      :   Any        =			nn.Parameter(old_model.in_proj_bias[:embed_dim]							)

																								elif attribute == "key_proj":
																														lowercase_      :   Any        =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]							)
																														lowercase_      :   List[str]        =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]							)
																								elif attribute == "value_proj":
																														lowercase_      :   Dict        =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]							)
																														lowercase_      :   str        =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]							)
																								lowercase_      :   List[Any]        =			True
																								break
																		elif attribute == "position_embeddings":
																								assert (
																								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																								), "Hidden size has to match"
																								assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
																								lowercase_      :   Dict        =			nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]							)
																								lowercase_      :   Union[str, Any]        =			True
																								break

																		if attribute.isdigit():
																								lowercase_      :   Dict        =			model[int(a							)]
																								lowercase_      :   Optional[int]        =			old_model[int(a							)]
																		else:
																								lowercase_      :   Any        =			getattr(a						,			a							)

																								if old_attribute == "":
																														lowercase_      :   Tuple        =			old_model
																								else:
																														if not hasattr(a						,			a							):
																																				raise ValueError(f"{old_model} does not have {old_attribute}"							)
																														lowercase_      :   str        =			getattr(a						,			a							)

												if not is_key_init:
																		raise ValueError(f"{key} was not correctly initialized!"							)

						print(f"Saving model to {pytorch_dump_folder_path}"							)
						prophet.save_pretrained(a							)


if __name__ == "__main__":
						A:      Optional[Any]  				=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						A:      Optional[Any]  				=				parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''

from typing import Dict, Optional

import numpy as np

import datasets


A:      int  				=				"\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:      List[str]  				=				"\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:      Union[str, 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 	_UpperCAmelCase    (  a		:	str						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			str:





						"""simple docstring"""
						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowercase_      :   Union[str, Any]        =			new_id

    # turn into Numpy arrays
						lowercase_      :   List[Any]        =			np.array(a							)
						lowercase_      :   Optional[Any]        =			np.array(a							)

						if reduce_labels:
												lowercase_      :   Any        =			2_5_5
												lowercase_      :   Dict        =			label - 1
												lowercase_      :   List[Any]        =			2_5_5

						lowercase_      :   Any        =			label != ignore_index
						lowercase_      :   List[Any]        =			np.not_equal(a						,			a							)
						lowercase_      :   Optional[int]        =			pred_label[mask]
						lowercase_      :   Union[str, Any]        =			np.array(a							)[mask]

						lowercase_      :   Optional[int]        =			pred_label[pred_label == label]

						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Optional[int]        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]
						lowercase_      :   Dict        =			np.histogram(a						,			bins=a						,			range=(0, num_labels - 1)							)[0]

						lowercase_      :   Optional[Any]        =			area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label






def 	_UpperCAmelCase    (  a		:	int						,			a		:	Optional[Any]						,			a		:	Optional[int]						,			a		:	bool						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Dict:





						"""simple docstring"""
						lowercase_      :   Dict        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[str]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   List[Any]        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						lowercase_      :   str        =			np.zeros((num_labels,)						,			dtype=np.floataa							)
						for result, gt_seg_map in zip(a						,			a							):
												lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Tuple        =			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 	_UpperCAmelCase    (  a		:	Optional[Any]						,			a		:	List[str]						,			a		:	Optional[Any]						,			a		:	bool						,			a		:	Optional[int] = None						,			a		:	Optional[Dict[int, int]] = None						,			a		:	bool = False						,			)			->			Optional[int]:





						"""simple docstring"""
						lowercase_  ,      lowercase_  ,      lowercase_  ,      lowercase_      :   Optional[Any]        =			total_intersect_and_union(
						    a						,			a						,			a						,			a						,			a						,			a							)

						# compute metrics
						lowercase_      :   str        =			{}

						lowercase_      :   str        =			total_area_intersect.sum() / total_area_label.sum()
						lowercase_      :   Optional[Any]        =			total_area_intersect / total_area_union
						lowercase_      :   List[Any]        =			total_area_intersect / total_area_label

						lowercase_      :   Any        =			np.nanmean(a							)
						lowercase_      :   Optional[Any]        =			np.nanmean(a							)
						lowercase_      :   int        =			all_acc
						lowercase_      :   Union[str, Any]        =			iou
						lowercase_      :   Optional[Any]        =			acc

						if nan_to_num is not None:
												lowercase_      :   Optional[int]        =			{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 							__magic_name__     (	datasets.Metric				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self       )						->  int:
							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        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = None  ,      _lowercase = None  ,      _lowercase = False  ,      )						->  Tuple:
							lowercase_      :   Optional[int]        =			mean_iou(
							    results=_lowercase  ,      gt_seg_maps=_lowercase  ,      num_labels=_lowercase  ,      ignore_index=_lowercase  ,      nan_to_num=_lowercase  ,      label_map=_lowercase  ,      reduce_labels=_lowercase  ,      )
							return iou_result
