


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

snake_case_  :	List[Any]							  =							{
    '''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   lowerCamelCase(      a__		,a__		,a__		,a__		,a__):
				for attribute in key.split('''.'''):
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,a__)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE	     =getattr(a__		,a__).shape
				else:
								_SCREAMING_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":
								_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
				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__):
				_SCREAMING_SNAKE_CASE	     =[]
				_SCREAMING_SNAKE_CASE	     =fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE	     =False
								if "conv_layers" in name:
												load_conv_layer(
												    a__		,a__		,a__		,a__		,hf_model.config.feat_extract_norm == '''group'''		,)
												_SCREAMING_SNAKE_CASE	     =True
								else:
												for key, mapped_key in MAPPING.items():
																_SCREAMING_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]:
																				_SCREAMING_SNAKE_CASE	     =True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE	     =name.split(a__)[0].split('''.''')[-2]
																								if not layer_index.isnumeric():
																												continue
																								_SCREAMING_SNAKE_CASE	     =mapped_key.replace('''*'''		,a__)
																				if "weight_g" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight_g'''
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight_v'''
																				elif "weight" in name:
																								_SCREAMING_SNAKE_CASE	     ='''weight'''
																				elif "bias" in name:
																								_SCREAMING_SNAKE_CASE	     ='''bias'''
																				else:
																								_SCREAMING_SNAKE_CASE	     =None
																				set_recursively(a__		,a__		,a__		,a__		,a__)
																continue
								if not is_used:
												unused_weights.append(a__)

				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:
												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."
												)
												_SCREAMING_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."
												)
												_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:
												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."
												)
												_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:
												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."
												)
												_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(a__)







def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =SEWDConfig()
				if is_finetuned:
								_SCREAMING_SNAKE_CASE	     =model.wav_encoder.wav_model.cfg
				else:
								_SCREAMING_SNAKE_CASE	     =model.cfg

				_SCREAMING_SNAKE_CASE	     =fs_config.conv_bias
				_SCREAMING_SNAKE_CASE	     =eval(fs_config.conv_feature_layers)
				_SCREAMING_SNAKE_CASE	     =[x[0] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     =[x[1] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     =[x[2] for x in conv_layers]
				_SCREAMING_SNAKE_CASE	     ='''gelu'''
				_SCREAMING_SNAKE_CASE	     ='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				_SCREAMING_SNAKE_CASE	     =0.0
				_SCREAMING_SNAKE_CASE	     =fs_config.activation_fn.name
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_embed_dim
				_SCREAMING_SNAKE_CASE	     =0.02
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_ffn_embed_dim
				_SCREAMING_SNAKE_CASE	     =1e-5
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_layerdrop
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_attention_heads
				_SCREAMING_SNAKE_CASE	     =fs_config.conv_pos_groups
				_SCREAMING_SNAKE_CASE	     =fs_config.conv_pos
				_SCREAMING_SNAKE_CASE	     =len(a__)
				_SCREAMING_SNAKE_CASE	     =fs_config.encoder_layers
				_SCREAMING_SNAKE_CASE	     =fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				_SCREAMING_SNAKE_CASE	     =fs_config.max_position_embeddings
				_SCREAMING_SNAKE_CASE	     =fs_config.position_buckets
				_SCREAMING_SNAKE_CASE	     =fs_config.share_att_key
				_SCREAMING_SNAKE_CASE	     =fs_config.relative_attention
				_SCREAMING_SNAKE_CASE	     =fs_config.position_biased_input
				_SCREAMING_SNAKE_CASE	     =tuple(fs_config.pos_att_type.split('''|'''))
				_SCREAMING_SNAKE_CASE	     =fs_config.norm_rel_ebd

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

				_SCREAMING_SNAKE_CASE	     ='''Wav2Vec2FeatureExtractor'''
				_SCREAMING_SNAKE_CASE	     ='''Wav2Vec2CTCTokenizer'''

				return config







@torch.no_grad()
def   lowerCamelCase(      a__		,a__		,a__=None		,a__=None		,a__=True):

				if is_finetuned:
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _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     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

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

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

				if is_finetuned:
								if dict_path:
												_SCREAMING_SNAKE_CASE	     =Dictionary.load(a__)

												# 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.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(a__		,'''vocab.json''')
												if not os.path.isdir(a__):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__))
																return
												os.makedirs(a__		,exist_ok=a__)
												with open(a__		,'''w'''		,encoding='''utf-8''') as vocab_handle:
																json.dump(target_dict.indices		,a__)
												_SCREAMING_SNAKE_CASE	     =WavaVecaCTCTokenizer(
												    a__		,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=a__		,)
												_SCREAMING_SNAKE_CASE	     =WavaVecaProcessor(feature_extractor=a__		,tokenizer=a__)
												processor.save_pretrained(a__)

								_SCREAMING_SNAKE_CASE	     =SEWDForCTC(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =SEWDModel(a__)
								feature_extractor.save_pretrained(a__)

				recursively_load_weights(a__		,a__		,a__)

				hf_model.save_pretrained(a__)


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



from __future__ import annotations

from collections.abc import Callable







def   lowerCamelCase(      a__		,a__		,a__		,a__ = 100		,):
				_SCREAMING_SNAKE_CASE	     =x_start
				_SCREAMING_SNAKE_CASE	     =fnc(a__)
				_SCREAMING_SNAKE_CASE	     =0.0

				for _ in range(a__):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_SCREAMING_SNAKE_CASE	     =(x_end - x_start) / steps + xa
								_SCREAMING_SNAKE_CASE	     =fnc(a__)
								area += abs(fxa + fxa) * (xa - xa) / 2

								# Increment step
								_SCREAMING_SNAKE_CASE	     =xa
								_SCREAMING_SNAKE_CASE	     =fxa
				return area


if __name__ == "__main__":







							def   lowerCamelCase(      a__):
											return x**3

							print('''f(x) = x^3''')
							print('''The area between the curve, x = -10, x = 10 and the x axis is:''')
							snake_case_  :	Any							  =							10
							while i <= 10_00_00:
														snake_case_  :	Tuple							  =							trapezoidal_area(f, -5, 5, i)
														print(f"""with {i} steps: {area}""")
														i *= 10