



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()
UpperCAmelCase							       =    logging.get_logger(__name__)

UpperCAmelCase							       =    {
    '''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 	A_					(		__a   : Optional[int]       ,						__a   : List[str]       ,						__a   : Tuple       ,						__a   : List[str]       ,						__a   : List[Any]						):

				"""simple docstring"""
				for attribute in key.split("""."""						):
								a__ 					=						getattr(a__       ,						a__						)

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

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

def 	A_					(		__a   : str       ,						__a   : Optional[int]       ,						__a   : Tuple						):

				"""simple docstring"""
				a__ 					=						[]
				a__ 					=						fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								a__ 					=						False
								if "conv_layers" in name:
												load_conv_layer(
												    a__       ,						a__       ,						a__       ,						a__       ,						hf_model.config.feat_extract_norm == """group"""       ,						)
												a__ 					=						True
								else:
												for key, mapped_key in MAPPING.items():
																a__ 					=						"""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]:
																				a__ 					=						True
																				if "*" in mapped_key:
																								a__ 					=						name.split(a__						)[0].split("""."""						)[-2]
																								if not layer_index.isnumeric():
																												continue
																								a__ 					=						mapped_key.replace("""*"""       ,						a__						)
																				if "weight_g" in name:
																								a__ 					=						"""weight_g"""
																				elif "weight_v" in name:
																								a__ 					=						"""weight_v"""
																				elif "weight" in name:
																								a__ 					=						"""weight"""
																				elif "bias" in name:
																								a__ 					=						"""bias"""
																				else:
																								a__ 					=						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 	A_					(		__a   : Tuple       ,						__a   : str       ,						__a   : int       ,						__a   : str       ,						__a   : int						):

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

def 	A_					(		__a   : Optional[int]       ,						__a   : Dict						):

				"""simple docstring"""
				a__ 					=						SEWDConfig()
				if is_finetuned:
								a__ 					=						model.wav_encoder.wav_model.cfg
				else:
								a__ 					=						model.cfg

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

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

				a__ 					=						"""Wav2Vec2FeatureExtractor"""
				a__ 					=						"""Wav2Vec2CTCTokenizer"""

				return config

@torch.no_grad()
def 	A_					(		__a   : Optional[Any]       ,						__a   : List[Any]       ,						__a   : Dict=None       ,						__a   : Union[str, Any]=None       ,						__a   : Dict=True						):

				"""simple docstring"""

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

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

				a__ 					=						True if config.feat_extract_norm == """layer""" else False
				a__ 					=						WavaVecaFeatureExtractor(
				    feature_size=1       ,						sampling_rate=16_000       ,						padding_value=0       ,						do_normalize=a__       ,						return_attention_mask=a__       ,						)

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

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

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

				recursively_load_weights(a__       ,						a__       ,						a__						)

				hf_model.save_pretrained(a__						)


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
	)





import math as m







def   lowerCamelCase(      a__		,a__		,a__=0.001):
				return (f(a + h) - f(a - h)) / (2 * h)







def   lowerCamelCase(      a__		,a__=0		,a__=100		,a__=0.0001		,a__=1e-6		,a__=False):
				_SCREAMING_SNAKE_CASE	     =xa  # set the initial guess
				_SCREAMING_SNAKE_CASE	     =[a]
				_SCREAMING_SNAKE_CASE	     =abs(f(a__))
				_SCREAMING_SNAKE_CASE	     =lambda a__: calc_derivative(a__		,a__		,h=a__)  # noqa: E731  Derivative of f(x)
				for _ in range(a__):
								if fa(a__) == 0:
												raise ValueError('''No converging solution found''')
								_SCREAMING_SNAKE_CASE	     =a - f(a__) / fa(a__)  # Calculate the next estimate
								if logsteps:
												steps.append(a__)
								if error < maxerror:
												break
				else:
								raise ValueError('''Iteration limit reached, no converging solution found''')
				if logsteps:
								# If logstep is true, then log intermediate steps
								return a, error, steps
				return a, error


if __name__ == "__main__":
							from matplotlib import pyplot as plt

							snake_case_  :	Tuple							  =							lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							snake_case_  , snake_case_  , snake_case_  :	str							  =							newton_raphson(
							    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
							)
							plt.plot([abs(f(x)) for x in steps])
							plt.xlabel('''step''')
							plt.ylabel('''error''')
							plt.show()
							print(f"""solution = {{{solution:f}}}, error = {{{error:f}}}""")