
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()
UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{
    '''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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	for attribute in key.split('.'				):
		lowercase	    =       getattr(_UpperCamelCase					,				_UpperCamelCase				)

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

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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       []
	lowercase	    =       fairseq_model.state_dict()

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

	for name, value in fairseq_dict.items():
		lowercase	    =       False
		if "conv_layers" in name:
			load_conv_layer(
			    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				hf_model.config.feat_extract_norm == 'group'					,				)
			lowercase	    =       True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase	    =       '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]:
					lowercase	    =       True
					if "*" in mapped_key:
						lowercase	    =       name.split(_UpperCamelCase				)[0].split('.'				)[-2]
						lowercase	    =       mapped_key.replace('*'					,				_UpperCamelCase				)
					if "weight_g" in name:
						lowercase	    =       'weight_g'
					elif "weight_v" in name:
						lowercase	    =       'weight_v'
					elif "weight" in name:
						lowercase	    =       'weight'
					elif "bias" in name:
						lowercase	    =       'bias'
					else:
						lowercase	    =       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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       full_name.split('conv_layers.'				)[-1]
	lowercase	    =       name.split('.'				)
	lowercase	    =       int(items[0]				)
	lowercase	    =       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.'''
			)
			lowercase	    =       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.'''
			)
			lowercase	    =       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."
			)
			lowercase	    =       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.'''
			)
			lowercase	    =       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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	lowercase	    =       SEWConfig()
	if is_finetuned:
		lowercase	    =       model.wav_encoder.wav_model.cfg
	else:
		lowercase	    =       model.cfg

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

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

	lowercase	    =       'Wav2Vec2FeatureExtractor'
	lowercase	    =       'Wav2Vec2CTCTokenizer'

	return config






@torch.no_grad()
def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=True				):

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

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

	lowercase	    =       True if config.feat_extract_norm == 'layer' else False
	lowercase	    =       WavaVecaFeatureExtractor(
	    feature_size=1					,				sampling_rate=1_6000					,				padding_value=0					,				do_normalize=_UpperCamelCase					,				return_attention_mask=_UpperCamelCase					,				)

	if is_finetuned:
		if dict_path:
			lowercase	    =       Dictionary.load(_UpperCamelCase				)

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

		lowercase	    =       SEWForCTC(_UpperCamelCase				)
	else:
		lowercase	    =       SEWModel(_UpperCamelCase				)
		feature_extractor.save_pretrained(_UpperCamelCase				)

	recursively_load_weights(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase				)

	hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
	UpperCAmelCase       =					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'''
	)
	UpperCAmelCase       =					parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)






def 		a__      (		_UpperCamelCase      :			list[int]				):
   if not numbers:
      return 0

   if not isinstance(_UpperCamelCase			,(list, tuple)				) or not all(
       isinstance(_UpperCamelCase			,_UpperCamelCase				) for number in numbers				):
      raise ValueError('''numbers must be an iterable of integers'''				)

   __lowerCamelCase    				=     __lowerCamelCase    				=     __lowerCamelCase    				=     numbers[0]

   for i in range(1			,len(_UpperCamelCase				)				):
      # update the maximum and minimum subarray products
      __lowerCamelCase    				=     numbers[i]
      if number < 0:
         __lowerCamelCase				,__lowerCamelCase    				=     min_till_now, max_till_now
      __lowerCamelCase    				=     max(_UpperCamelCase			,max_till_now * number				)
      __lowerCamelCase    				=     min(_UpperCamelCase			,min_till_now * number				)

      # update the maximum product found till now
      __lowerCamelCase    				=     max(_UpperCamelCase			,_UpperCamelCase				)

   return max_prod






