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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE:						List[str]    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Dict    	=       {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			for attribute in key.split("."  ):
						_snake_case              =      getattr(UpperCamelCase_							,	UpperCamelCase_  )

			if weight_type is not None:
						_snake_case              =      getattr(UpperCamelCase_							,	UpperCamelCase_  ).shape
			else:
						_snake_case              =      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":
						_snake_case              =      value
			elif weight_type == "weight_g":
						_snake_case              =      value
			elif weight_type == "weight_v":
						_snake_case              =      value
			elif weight_type == "bias":
						_snake_case              =      value
			else:
						_snake_case              =      value

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






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      []
			_snake_case              =      fairseq_model.state_dict()

			_snake_case              =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						_snake_case              =      False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase_							,	UpperCamelCase_							,	UpperCamelCase_							,	UpperCamelCase_							,	hf_model.config.feat_extract_norm == "group"							,	)
									_snake_case              =      True
						else:
									for key, mapped_key in MAPPING.items():
												_snake_case              =      "sew_d." + 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]:
															_snake_case              =      True
															if "*" in mapped_key:
																		_snake_case              =      name.split(UpperCamelCase_  )[0].split("."  )[-2]
																		if not layer_index.isnumeric():
																					continue
																		_snake_case              =      mapped_key.replace("*"							,	UpperCamelCase_  )
															if "weight_g" in name:
																		_snake_case              =      "weight_g"
															elif "weight_v" in name:
																		_snake_case              =      "weight_v"
															elif "weight" in name:
																		_snake_case              =      "weight"
															elif "bias" in name:
																		_snake_case              =      "bias"
															else:
																		_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      full_name.split("conv_layers."  )[-1]
			_snake_case              =      name.split("."  )
			_snake_case              =      int(items[0]  )
			_snake_case              =      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.'
									)
									_snake_case              =      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.'
									)
									_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:
									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."
									)
									_snake_case              =      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.'
									)
									_snake_case              =      value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
			else:
						unused_weights.append(UpperCamelCase_  )






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      SEWDConfig()
			if is_finetuned:
						_snake_case              =      model.wav_encoder.wav_model.cfg
			else:
						_snake_case              =      model.cfg

			_snake_case              =      fs_config.conv_bias
			_snake_case              =      eval(fs_config.conv_feature_layers  )
			_snake_case              =      [x[0] for x in conv_layers]
			_snake_case              =      [x[1] for x in conv_layers]
			_snake_case              =      [x[2] for x in conv_layers]
			_snake_case              =      "gelu"
			_snake_case              =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
			_snake_case              =      0.0
			_snake_case              =      fs_config.activation_fn.name
			_snake_case              =      fs_config.encoder_embed_dim
			_snake_case              =      0.0_2
			_snake_case              =      fs_config.encoder_ffn_embed_dim
			_snake_case              =      1E-5
			_snake_case              =      fs_config.encoder_layerdrop
			_snake_case              =      fs_config.encoder_attention_heads
			_snake_case              =      fs_config.conv_pos_groups
			_snake_case              =      fs_config.conv_pos
			_snake_case              =      len(UpperCamelCase_  )
			_snake_case              =      fs_config.encoder_layers
			_snake_case              =      fs_config.squeeze_factor
			# DeBERTa-specific parameters:
			_snake_case              =      fs_config.max_position_embeddings
			_snake_case              =      fs_config.position_buckets
			_snake_case              =      fs_config.share_att_key
			_snake_case              =      fs_config.relative_attention
			_snake_case              =      fs_config.position_biased_input
			_snake_case              =      tuple(fs_config.pos_att_type.split("|"  )  )
			_snake_case              =      fs_config.norm_rel_ebd

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

			_snake_case              =      "Wav2Vec2FeatureExtractor"
			_snake_case              =      "Wav2Vec2CTCTokenizer"

			return config






@torch.no_grad()
def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_=None							,	lowerCamelCase_=None							,	lowerCamelCase_=True  ):



			"""simple docstring"""





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

			if config_path is not None:
						_snake_case              =      SEWDConfig.from_pretrained(UpperCamelCase_  )
			else:
						_snake_case              =      convert_config(model[0]							,	UpperCamelCase_  )
			_snake_case              =      model[0].eval()

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

			if is_finetuned:
						if dict_path:
									_snake_case              =      Dictionary.load(UpperCamelCase_  )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									_snake_case              =      target_dict.pad_index
									_snake_case              =      target_dict.bos_index
									_snake_case              =      target_dict.pad_index
									_snake_case              =      target_dict.bos_index
									_snake_case              =      target_dict.eos_index
									_snake_case              =      len(target_dict.symbols  )
									_snake_case              =      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_  )
									_snake_case              =      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_							,	)
									_snake_case              =      WavaVecaProcessor(feature_extractor=UpperCamelCase_							,	tokenizer=UpperCamelCase_  )
									processor.save_pretrained(UpperCamelCase_  )

						_snake_case              =      SEWDForCTC(UpperCamelCase_  )
			else:
						_snake_case              =      SEWDModel(UpperCamelCase_  )
						feature_extractor.save_pretrained(UpperCamelCase_  )

			recursively_load_weights(UpperCamelCase_							,	UpperCamelCase_							,	UpperCamelCase_  )

			hf_model.save_pretrained(UpperCamelCase_  )


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:						List[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'''
			)
			_SCREAMING_SNAKE_CASE:						Optional[int]    	=       parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

