

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a__												=						{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''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''',
}
a__												=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Any						,__a :     Dict						,__a :     List[str]						,__a :     Tuple      )     ->	Union[str, Any]:

					"""simple docstring"""

					for attribute in key.split('''.'''      ):
										_a      :       Tuple   =						getattr(__a						,__a      )

					if weight_type is not None:
										_a      :       Union[str, Any]   =						getattr(__a						,__a      ).shape
					else:
										_a      :       Optional[Any]   =						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":
										_a      :       str   =						value
					elif weight_type == "weight_g":
										_a      :       str   =						value
					elif weight_type == "weight_v":
										_a      :       Optional[int]   =						value
					elif weight_type == "bias":
										_a      :       List[str]   =						value
					elif weight_type == "running_mean":
										_a      :       List[str]   =						value
					elif weight_type == "running_var":
										_a      :       List[str]   =						value
					elif weight_type == "num_batches_tracked":
										_a      :       Tuple   =						value
					elif weight_type == "inv_freq":
										_a      :       Tuple   =						value
					else:
										_a      :       Tuple   =						value

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



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     str						,__a :     Tuple      )     ->	Any:

					"""simple docstring"""

					_a      :       str   =						[]
					_a      :       str   =						fairseq_model.state_dict()

					_a      :       Optional[Any]   =						hf_model.wavaveca_conformer.feature_extractor

					for name, value in fairseq_dict.items():
										_a      :       Any   =						False
										if "conv_layers" in name:
															load_conv_layer(
															    __a						,__a						,__a						,__a						,hf_model.config.feat_extract_norm == '''group'''						,)
															_a      :       List[Any]   =						True
										else:
															for key, mapped_key in MAPPING.items():
																				_a      :       List[Any]   =						'''wav2vec2_conformer.''' + 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]:
																									_a      :       Optional[Any]   =						True
																									if "*" in mapped_key:
																														_a      :       List[Any]   =						name.split(__a      )[0].split('''.'''      )[-2]
																														_a      :       Optional[Any]   =						mapped_key.replace('''*'''						,__a      )
																									if "pos_bias_u" in name:
																														_a      :       List[str]   =						None
																									elif "pos_bias_v" in name:
																														_a      :       Tuple   =						None
																									elif "weight_g" in name:
																														_a      :       List[str]   =						'''weight_g'''
																									elif "weight_v" in name:
																														_a      :       Optional[int]   =						'''weight_v'''
																									elif "bias" in name:
																														_a      :       Tuple   =						'''bias'''
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														_a      :       int   =						'''weight'''
																									elif "running_mean" in name:
																														_a      :       Any   =						'''running_mean'''
																									elif "inv_freq" in name:
																														_a      :       Dict   =						'''inv_freq'''
																									elif "running_var" in name:
																														_a      :       int   =						'''running_var'''
																									elif "num_batches_tracked" in name:
																														_a      :       Optional[Any]   =						'''num_batches_tracked'''
																									else:
																														_a      :       List[str]   =						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   __UpperCAmelCase					(      __a :     int						,__a :     str						,__a :     Optional[int]						,__a :     List[str]						,__a :     List[str]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Tuple   =						full_name.split('''conv_layers.'''      )[-1]
					_a      :       List[Any]   =						name.split('''.'''      )
					_a      :       Union[str, Any]   =						int(items[0]      )
					_a      :       Union[str, Any]   =						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."""      )
															_a      :       int   =						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."""      )
															_a      :       int   =						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."""      )
															_a      :       int   =						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."""      )
															_a      :       int   =						value
															logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
					else:
										unused_weights.append(__a      )



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Tuple						,__a :     str						,__a :     Optional[int]=None						,__a :     Dict=None						,__a :     str=True      )     ->	Optional[int]:

					"""simple docstring"""

					if config_path is not None:
										_a      :       Any   =						WavaVecaConformerConfig.from_pretrained(__a						,hidden_act='''swish'''      )
					else:
										_a      :       str   =						WavaVecaConformerConfig()

					if "rope" in checkpoint_path:
										_a      :       Optional[Any]   =						'''rotary'''

					if is_finetuned:
										if dict_path:
															_a      :       Dict   =						Dictionary.load(__a      )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															_a      :       int   =						target_dict.pad_index
															_a      :       Optional[int]   =						target_dict.bos_index
															_a      :       List[str]   =						target_dict.eos_index
															_a      :       Any   =						len(target_dict.symbols      )
															_a      :       Any   =						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      )
															_a      :       Optional[int]   =						target_dict.indices

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

										_a      :       Optional[Any]   =						WavaVecaConformerForCTC(__a      )
					else:
										_a      :       Union[str, Any]   =						WavaVecaConformerForPreTraining(__a      )

					if is_finetuned:
										_a ,   _a ,   _a      :       Any   =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]						,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
					else:
										_a      :       Union[str, Any]   =						argparse.Namespace(task='''audio_pretraining'''      )
										_a      :       Dict   =						fairseq.tasks.setup_task(__a      )

										_a ,   _a ,   _a      :       Optional[int]   =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,task=__a      )

					_a      :       List[str]   =						model[0].eval()

					recursively_load_weights(__a						,__a						,not is_finetuned      )

					hf_wavavec.save_pretrained(__a      )


if __name__ == "__main__":
			a__												=						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'''
			)
			a__												=						parser.parse_args()
			convert_wavaveca_conformer_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)










"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
