


'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_  :				Dict						= logging.get_logger(__name__)

a_  :				Tuple						= {
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Optional[Any]		):
 for attribute in key.split("."		):
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		)

 if weight_type is not None:
  lowerCamelCase_      =  getattr(UpperCAmelCase_					,				UpperCAmelCase_		).shape
 else:
  lowerCamelCase_      =  hf_pointer.shape

 assert hf_shape == value.shape, (
     F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
     F''' {value.shape} for {full_name}'''
 )

 if weight_type == "weight":
  lowerCamelCase_      =  value
 elif weight_type == "weight_g":
  lowerCamelCase_      =  value
 elif weight_type == "weight_v":
  lowerCamelCase_      =  value
 elif weight_type == "bias":
  lowerCamelCase_      =  value
 else:
  lowerCamelCase_      =  value

 logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  []
 lowerCamelCase_      =  fairseq_model.state_dict()

 lowerCamelCase_      =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  lowerCamelCase_      =  False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				hf_model.config.feat_extract_norm == "group"					,				)
   lowerCamelCase_      =  True
  else:
   for key, mapped_key in MAPPING.items():
    lowerCamelCase_      =  "sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

    if key in name or key.split("w2v_model."		)[-1] == name.split("."		)[0]:
     lowerCamelCase_      =  True
     if "*" in mapped_key:
      lowerCamelCase_      =  name.split(UpperCAmelCase_		)[0].split("."		)[-2]
      lowerCamelCase_      =  mapped_key.replace("*"					,				UpperCAmelCase_		)
     if "weight_g" in name:
      lowerCamelCase_      =  "weight_g"
     elif "weight_v" in name:
      lowerCamelCase_      =  "weight_v"
     elif "weight" in name:
      lowerCamelCase_      =  "weight"
     elif "bias" in name:
      lowerCamelCase_      =  "bias"
     else:
      lowerCamelCase_      =  None
     set_recursively(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
    continue
  if not is_used:
   unused_weights.append(UpperCAmelCase_		)

 logger.warning(F'''Unused weights: {unused_weights}'''		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 lowerCamelCase_      =  full_name.split("conv_layers."		)[-1]
 lowerCamelCase_      =  name.split("."		)
 lowerCamelCase_      =  int(items[0]		)
 lowerCamelCase_      =  int(items[1]		)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
   )
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
   )
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
       " found."
   )
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       F'''{full_name} has size {value.shape}, but'''
       F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
   )
   lowerCamelCase_      =  value
   logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
 else:
  unused_weights.append(UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  SEWConfig()
 if is_finetuned:
  lowerCamelCase_      =  model.wav_encoder.wav_model.cfg
 else:
  lowerCamelCase_      =  model.cfg

 lowerCamelCase_      =  fs_config.conv_bias
 lowerCamelCase_      =  eval(fs_config.conv_feature_layers		)
 lowerCamelCase_      =  [x[0] for x in conv_layers]
 lowerCamelCase_      =  [x[1] for x in conv_layers]
 lowerCamelCase_      =  [x[2] for x in conv_layers]
 lowerCamelCase_      =  "gelu"
 lowerCamelCase_      =  "layer" if fs_config.extractor_mode == "layer_norm" else "group"
 lowerCamelCase_      =  0.0
 lowerCamelCase_      =  fs_config.activation_fn.name
 lowerCamelCase_      =  fs_config.encoder_embed_dim
 lowerCamelCase_      =  0.02
 lowerCamelCase_      =  fs_config.encoder_ffn_embed_dim
 lowerCamelCase_      =  1E-5
 lowerCamelCase_      =  fs_config.encoder_layerdrop
 lowerCamelCase_      =  fs_config.encoder_attention_heads
 lowerCamelCase_      =  fs_config.conv_pos_groups
 lowerCamelCase_      =  fs_config.conv_pos
 lowerCamelCase_      =  len(UpperCAmelCase_		)
 lowerCamelCase_      =  fs_config.encoder_layers
 lowerCamelCase_      =  fs_config.squeeze_factor

 # take care of any params that are overridden by the Wav2VecCtc model
 if is_finetuned:
  lowerCamelCase_      =  model.cfg
  lowerCamelCase_      =  fs_config.final_dropout
  lowerCamelCase_      =  fs_config.layerdrop
 lowerCamelCase_      =  fs_config.activation_dropout
 lowerCamelCase_      =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
 lowerCamelCase_      =  fs_config.attention_dropout
 lowerCamelCase_      =  fs_config.dropout_input
 lowerCamelCase_      =  fs_config.dropout
 lowerCamelCase_      =  fs_config.mask_channel_length
 lowerCamelCase_      =  fs_config.mask_channel_prob
 lowerCamelCase_      =  fs_config.mask_length
 lowerCamelCase_      =  fs_config.mask_prob

 lowerCamelCase_      =  "Wav2Vec2FeatureExtractor"
 lowerCamelCase_      =  "Wav2Vec2CTCTokenizer"

 return config





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[str]=None					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						int=True		):

 if is_finetuned:
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]					,				arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
 else:
  lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

 if config_path is not None:
  lowerCamelCase_      =  SEWConfig.from_pretrained(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  convert_config(model[0]					,				UpperCAmelCase_		)
 lowerCamelCase_      =  model[0].eval()

 lowerCamelCase_      =  True if config.feat_extract_norm == "layer" else False
 lowerCamelCase_      =  WavaVecaFeatureExtractor(
     feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=UpperCAmelCase_					,				return_attention_mask=UpperCAmelCase_					,				)

 if is_finetuned:
  if dict_path:
   lowerCamelCase_      =  Dictionary.load(UpperCAmelCase_		)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   lowerCamelCase_      =  target_dict.pad_index
   lowerCamelCase_      =  target_dict.bos_index
   lowerCamelCase_      =  target_dict.pad_index
   lowerCamelCase_      =  target_dict.bos_index
   lowerCamelCase_      =  target_dict.eos_index
   lowerCamelCase_      =  len(target_dict.symbols		)
   lowerCamelCase_      =  os.path.join(UpperCAmelCase_					,				"vocab.json"		)
   if not os.path.isdir(UpperCAmelCase_		):
    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_		)		)
    return
   os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_		)
   with open(UpperCAmelCase_					,				"w"					,				encoding="utf-8"		) as vocab_handle:
    json.dump(target_dict.indices					,				UpperCAmelCase_		)
   lowerCamelCase_      =  WavaVecaCTCTokenizer(
       UpperCAmelCase_					,				unk_token=target_dict.unk_word					,				pad_token=target_dict.pad_word					,				bos_token=target_dict.bos_word					,				eos_token=target_dict.eos_word					,				word_delimiter_token="|"					,				do_lower_case=UpperCAmelCase_					,				)
   lowerCamelCase_      =  WavaVecaProcessor(feature_extractor=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_		)
   processor.save_pretrained(UpperCAmelCase_		)

  lowerCamelCase_      =  SEWForCTC(UpperCAmelCase_		)
 else:
  lowerCamelCase_      =  SEWModel(UpperCAmelCase_		)
  feature_extractor.save_pretrained(UpperCAmelCase_		)

 recursively_load_weights(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 hf_model.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Any						= argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     a_  :				int						= parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )





from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE							:List[Any]							 =				'\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'

SCREAMING_SNAKE_CASE							: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'

SCREAMING_SNAKE_CASE							:str							 =				'\\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_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Tuple:







				"""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   			= 2_5_5
								__A   			= label - 1
								__A   			= 2_5_5

				__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 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Union[str, Any]:







				"""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 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   )		->	str:







				"""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 							UpperCAmelCase      (  datasets.Metric  ):




	'''simple docstring'''





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