
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__							=				[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = {}
		with open(lowerCAmelCase_,							'''r'''    ) as file:
				for line_number, line in enumerate(lowerCAmelCase_    ):
						SCREAMING_SNAKE_CASE_						 = line.strip()
						if line:
								SCREAMING_SNAKE_CASE_						 = line.split()
								SCREAMING_SNAKE_CASE_						 = line_number
								SCREAMING_SNAKE_CASE_						 = words[0]
								SCREAMING_SNAKE_CASE_						 = value
		return result



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):



		for attribute in key.split('''.'''    ):
				SCREAMING_SNAKE_CASE_						 = getattr(lowerCAmelCase_,							lowerCAmelCase_    )

		SCREAMING_SNAKE_CASE_						 = None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(lowerCAmelCase_    ):
						SCREAMING_SNAKE_CASE_						 = PARAM_MAPPING[full_name.split('''.'''    )[-1]]
						SCREAMING_SNAKE_CASE_						 = '''param'''

		if weight_type is not None and weight_type != "param":
				SCREAMING_SNAKE_CASE_						 = getattr(lowerCAmelCase_,							lowerCAmelCase_    ).shape
		elif weight_type is not None and weight_type == "param":
				SCREAMING_SNAKE_CASE_						 = hf_pointer
				for attribute in hf_param_name.split('''.'''    ):
						SCREAMING_SNAKE_CASE_						 = getattr(lowerCAmelCase_,							lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE_						 = shape_pointer.shape

				# let's reduce dimension
				SCREAMING_SNAKE_CASE_						 = value[0]
		else:
				SCREAMING_SNAKE_CASE_						 = hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_						 = value
		elif weight_type == "param":
				for attribute in hf_param_name.split('''.'''    ):
						SCREAMING_SNAKE_CASE_						 = getattr(lowerCAmelCase_,							lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE_						 = value
		else:
				SCREAMING_SNAKE_CASE_						 = value

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



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(lowerCAmelCase_    ):
						SCREAMING_SNAKE_CASE_						 = PARAM_MAPPING[full_name.split('''.'''    )[-1]]
						SCREAMING_SNAKE_CASE_						 = '''param'''

		if weight_type is not None and weight_type != "param":
				SCREAMING_SNAKE_CASE_						 = '''.'''.join([key, weight_type]    )
		elif weight_type is not None and weight_type == "param":
				SCREAMING_SNAKE_CASE_						 = '''.'''.join([key, hf_param_name]    )
		else:
				SCREAMING_SNAKE_CASE_						 = key

		SCREAMING_SNAKE_CASE_						 = value if '''lm_head''' in full_key else value[0]


lowerCAmelCase__							=				{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}



def        _lowerCamelCase ( __a,							__a,							__a=None,							__a=None    ):
		SCREAMING_SNAKE_CASE_						 = False
		for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE_						 = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
						SCREAMING_SNAKE_CASE_						 = True
						if "*" in mapped_key:
								SCREAMING_SNAKE_CASE_						 = name.split(lowerCAmelCase_    )[0].split('''.'''    )[-2]
								SCREAMING_SNAKE_CASE_						 = mapped_key.replace('''*''',							lowerCAmelCase_    )
						if "weight_g" in name:
								SCREAMING_SNAKE_CASE_						 = '''weight_g'''
						elif "weight_v" in name:
								SCREAMING_SNAKE_CASE_						 = '''weight_v'''
						elif "bias" in name:
								SCREAMING_SNAKE_CASE_						 = '''bias'''
						elif "weight" in name:
								# TODO: don't match quantizer.weight_proj
								SCREAMING_SNAKE_CASE_						 = '''weight'''
						else:
								SCREAMING_SNAKE_CASE_						 = None
						if hf_dict is not None:
								rename_dict(lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_    )
						else:
								set_recursively(lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_    )
						return is_used
		return is_used



def        _lowerCamelCase ( __a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = []
		SCREAMING_SNAKE_CASE_						 = fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE_						 = hf_model.wavaveca.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE_						 = False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_,							hf_model.config.feat_extract_norm == '''group''',							)
						SCREAMING_SNAKE_CASE_						 = True
				else:
						SCREAMING_SNAKE_CASE_						 = load_wavaveca_layer(lowerCAmelCase_,							lowerCAmelCase_,							lowerCAmelCase_    )
				if not is_used:
						unused_weights.append(lowerCAmelCase_    )

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



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = full_name.split('''conv_layers.'''    )[-1]
		SCREAMING_SNAKE_CASE_						 = name.split('''.'''    )
		SCREAMING_SNAKE_CASE_						 = int(items[0]    )
		SCREAMING_SNAKE_CASE_						 = int(items[1]    )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'    )
						SCREAMING_SNAKE_CASE_						 = value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'    )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'    )
						SCREAMING_SNAKE_CASE_						 = 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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'    )
						SCREAMING_SNAKE_CASE_						 = value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'    )
						SCREAMING_SNAKE_CASE_						 = value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
		else:
				unused_weights.append(lowerCAmelCase_    )



@torch.no_grad()
def        _lowerCamelCase ( __a,							__a,							__a=None,							__a=None,							__a=True,							__a=False    ):



		if config_path is not None:
				SCREAMING_SNAKE_CASE_						 = WavaVecaConfig.from_pretrained(lowerCAmelCase_    )
		else:
				SCREAMING_SNAKE_CASE_						 = WavaVecaConfig()

		if is_seq_class:
				SCREAMING_SNAKE_CASE_						 = read_txt_into_dict(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE_						 = idalabel
				SCREAMING_SNAKE_CASE_						 = WavaVecaForSequenceClassification(lowerCAmelCase_    )
				SCREAMING_SNAKE_CASE_						 = WavaVecaFeatureExtractor(
				    feature_size=1,							sampling_rate=16_000,							padding_value=0,							do_normalize=lowerCAmelCase_,							return_attention_mask=lowerCAmelCase_,							)
				feature_extractor.save_pretrained(lowerCAmelCase_    )

		elif is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE_						 = Dictionary.load(lowerCAmelCase_    )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						SCREAMING_SNAKE_CASE_						 = target_dict.pad_index
						SCREAMING_SNAKE_CASE_						 = target_dict.bos_index
						SCREAMING_SNAKE_CASE_						 = target_dict.eos_index
						SCREAMING_SNAKE_CASE_						 = len(target_dict.symbols    )
						SCREAMING_SNAKE_CASE_						 = os.path.join(lowerCAmelCase_,							'''vocab.json'''    )
						if not os.path.isdir(lowerCAmelCase_    ):
								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_    )    )
								return
						os.makedirs(lowerCAmelCase_,							exist_ok=lowerCAmelCase_    )
						SCREAMING_SNAKE_CASE_						 = target_dict.indices

						# fairseq has the <pad> and <s> switched
						SCREAMING_SNAKE_CASE_						 = 0
						SCREAMING_SNAKE_CASE_						 = 1
						with open(lowerCAmelCase_,							'''w''',							encoding='''utf-8'''    ) as vocab_handle:
								json.dump(lowerCAmelCase_,							lowerCAmelCase_    )
						SCREAMING_SNAKE_CASE_						 = WavaVecaCTCTokenizer(
						    lowerCAmelCase_,							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=lowerCAmelCase_,							)
						SCREAMING_SNAKE_CASE_						 = True if config.feat_extract_norm == '''layer''' else False
						SCREAMING_SNAKE_CASE_						 = WavaVecaFeatureExtractor(
						    feature_size=1,							sampling_rate=16_000,							padding_value=0,							do_normalize=lowerCAmelCase_,							return_attention_mask=lowerCAmelCase_,							)
						SCREAMING_SNAKE_CASE_						 = WavaVecaProcessor(feature_extractor=lowerCAmelCase_,							tokenizer=lowerCAmelCase_    )
						processor.save_pretrained(lowerCAmelCase_    )

				SCREAMING_SNAKE_CASE_						 = WavaVecaForCTC(lowerCAmelCase_    )
		else:
				SCREAMING_SNAKE_CASE_						 = WavaVecaForPreTraining(lowerCAmelCase_    )

		if is_finetuned or is_seq_class:
				SCREAMING_SNAKE_CASE_						 = fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path],							arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
		else:
				SCREAMING_SNAKE_CASE_						 = argparse.Namespace(task='''audio_pretraining'''    )
				SCREAMING_SNAKE_CASE_						 = fairseq.tasks.setup_task(lowerCAmelCase_    )

				SCREAMING_SNAKE_CASE_						 = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],							task=lowerCAmelCase_    )

		SCREAMING_SNAKE_CASE_						 = model[0].eval()

		recursively_load_weights(lowerCAmelCase_,							lowerCAmelCase_,							not is_finetuned    )

		hf_wavavec.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
						lowerCAmelCase__							=				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(
						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						parser.add_argument(
						    '--is_seq_class',
						    action='store_true',
						    help='Whether the model to convert is a fine-tuned sequence classification model or not',
						)
						lowerCAmelCase__							=				parser.parse_args()

						lowerCAmelCase__							=				not args.not_finetuned and not args.is_seq_class
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.dict_path,
						    is_finetuned,
						    args.is_seq_class,
						)

"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase__							=				{
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase__							=				logging.get_logger(__name__)





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''maskformer'''
							UpperCAmelCase__						     =      {'''hidden_size''': '''mask_feature_size'''}
							UpperCAmelCase__						     =      ['''resnet''', '''swin''']
							UpperCAmelCase__						     =      ['''detr''']

							def __init__(self       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 2_56       ,       SCREAMING_SNAKE_CASE_ = 0.1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = 0.02       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 1.0       ,       SCREAMING_SNAKE_CASE_ = 20.0       ,       SCREAMING_SNAKE_CASE_ = None       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									if backbone_config is None:
											# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
											SCREAMING_SNAKE_CASE_						 = SwinConfig(
											    image_size=3_84       ,       in_channels=3       ,       patch_size=4       ,       embed_dim=1_28       ,       depths=[2, 2, 18, 2]       ,       num_heads=[4, 8, 16, 32]       ,       window_size=12       ,       drop_path_rate=0.3       ,       out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       ,       )

									if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
											SCREAMING_SNAKE_CASE_						 = backbone_config.pop('''model_type'''    )
											SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
											    f'Supported model types: {",".join(self.backbones_supported    )}'    )

									if decoder_config is None:
											# fall back to https://huggingface.co/facebook/detr-resnet-50
											SCREAMING_SNAKE_CASE_						 = DetrConfig()
									else:
											# verify that the decoder is supported
											SCREAMING_SNAKE_CASE_						 = (
											    decoder_config.pop('''model_type'''    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else decoder_config.model_type
											)
											if decoder_type not in self.decoders_supported:
													raise ValueError(
													    f'Transformer Decoder {decoder_type} not supported, please use one of'
													    f' {",".join(self.decoders_supported    )}'    )
											if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):
													SCREAMING_SNAKE_CASE_						 = CONFIG_MAPPING[decoder_type]
													SCREAMING_SNAKE_CASE_						 = config_class.from_dict(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = backbone_config
									SCREAMING_SNAKE_CASE_						 = decoder_config
									# main feature dimension for the model
									SCREAMING_SNAKE_CASE_						 = fpn_feature_size
									SCREAMING_SNAKE_CASE_						 = mask_feature_size
									# initializer
									SCREAMING_SNAKE_CASE_						 = init_std
									SCREAMING_SNAKE_CASE_						 = init_xavier_std
									# Hungarian matcher && loss
									SCREAMING_SNAKE_CASE_						 = cross_entropy_weight
									SCREAMING_SNAKE_CASE_						 = dice_weight
									SCREAMING_SNAKE_CASE_						 = mask_weight
									SCREAMING_SNAKE_CASE_						 = use_auxiliary_loss
									SCREAMING_SNAKE_CASE_						 = no_object_weight
									SCREAMING_SNAKE_CASE_						 = output_auxiliary_logits

									SCREAMING_SNAKE_CASE_						 = self.decoder_config.encoder_attention_heads
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.num_hidden_layers
									super().__init__(**SCREAMING_SNAKE_CASE_    )

							@classmethod
							def _lowercase					(cls       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return cls(
									    backbone_config=SCREAMING_SNAKE_CASE_       ,       decoder_config=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_       ,       )







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = copy.deepcopy(self.__dict__    )
									SCREAMING_SNAKE_CASE_						 = self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.decoder_config.to_dict()
									SCREAMING_SNAKE_CASE_						 = self.__class__.model_type
									return output