





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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[int]:
       for attribute in key.split("."	):
              UpperCamelCase_ 							=	getattr(UpperCamelCase_       ,		UpperCamelCase_	)

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

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	[]
       UpperCamelCase_ 							=	fairseq_model.state_dict()

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

       for name, value in fairseq_dict.items():
              UpperCamelCase_ 							=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		hf_model.config.feat_extract_norm == "group"       ,		)
                     UpperCamelCase_ 							=	True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCamelCase_ 							=	"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]:
                                   UpperCamelCase_ 							=	True
                                   if "*" in mapped_key:
                                          UpperCamelCase_ 							=	name.split(UpperCamelCase_	)[0].split("."	)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          UpperCamelCase_ 							=	mapped_key.replace("*"       ,		UpperCamelCase_	)
                                   if "weight_g" in name:
                                          UpperCamelCase_ 							=	"weight_g"
                                   elif "weight_v" in name:
                                          UpperCamelCase_ 							=	"weight_v"
                                   elif "weight" in name:
                                          UpperCamelCase_ 							=	"weight"
                                   elif "bias" in name:
                                          UpperCamelCase_ 							=	"bias"
                                   else:
                                          UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	full_name.split("conv_layers."	)[-1]
       UpperCamelCase_ 							=	name.split("."	)
       UpperCamelCase_ 							=	int(items[0]	)
       UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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."
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	SEWDConfig()
       if is_finetuned:
              UpperCamelCase_ 							=	model.wav_encoder.wav_model.cfg
       else:
              UpperCamelCase_ 							=	model.cfg

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

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

       UpperCamelCase_ 							=	"Wav2Vec2FeatureExtractor"
       UpperCamelCase_ 							=	"Wav2Vec2CTCTokenizer"

       return config




@torch.no_grad()
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=None       ,		UpperCamelCase_=None       ,		UpperCamelCase_=True	)  ->		Union[str, Any]:

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

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

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

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

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

              UpperCamelCase_ 							=	SEWDForCTC(UpperCamelCase_	)
       else:
              UpperCamelCase_ 							=	SEWDModel(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
     )






from typing import Any

import numpy as np




def    UpperCAmelCase					(  lowercase__						:					np.ndarray	):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]

			# If matrix not symmetric, exit right away.
			if np.allclose(lowercase__				,					matrix.T	) is False:
						return False

			# Get eigenvalues and eignevectors for a symmetric matrix.
			a__				,       a__					=  np.linalg.eigh(lowercase__	)

			# Check sign of all eigenvalues.
			# np.all returns a value of type np.bool_
			return bool(np.all(eigen_values > 0	)	)




def    UpperCAmelCase					(  lowercase__						:					int	):



			'''simple docstring'''





			a__					=  np.random.randn(lowercase__				,					lowercase__	)
			a__					=  np.dot(lowercase__				,					random_matrix.T	)
			assert _is_matrix_spd(lowercase__	)
			return spd_matrix




def    UpperCAmelCase					(  lowercase__						:					np.ndarray				,					lowercase__						:					np.ndarray				,					lowercase__						:					int = 1000				,					lowercase__						:					float = 1e-8				,					):



			'''simple docstring'''





			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[1]
			assert np.shape(lowercase__	)[0] == np.shape(lowercase__	)[0]
			assert _is_matrix_spd(lowercase__	)

			# Initialize solution guess, residual, search direction.
			a__					=  np.zeros((np.shape(lowercase__	)[0], 1)	)
			a__					=  np.copy(lowercase__	)
			a__					=  np.copy(lowercase__	)

			# Set initial errors in solution guess and residual.
			a__					=  1e9
			a__					=  1e9
			a__					=  1e9

			# Set iteration counter to threshold number of iterations.
			a__					=  0

			while error > tol:
						# Save this value so we only calculate the matrix-vector product once.
						a__					=  np.dot(lowercase__				,					lowercase__	)

						# The main algorithm.

						# Update search direction magnitude.
						a__					=  np.dot(ra.T				,					lowercase__	) / np.dot(pa.T				,					lowercase__	)
						# Update solution guess.
						a__					=  xa + alpha * pa
						# Calculate new residual.
						a__					=  ra - alpha * w
						# Calculate new Krylov subspace scale.
						a__					=  np.dot(r.T				,					lowercase__	) / np.dot(ra.T				,					lowercase__	)
						# Calculate new A conjuage search direction.
						a__					=  r + beta * pa

						# Calculate errors.
						a__					=  np.linalg.norm(r - ra	)
						a__					=  np.linalg.norm(x - xa	)
						a__					=  np.maximum(lowercase__				,					lowercase__	)

						# Update variables.
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)
						a__					=  np.copy(lowercase__	)

						# Update number of iterations.
						iterations += 1
						if iterations > max_iterations:
									break

			return x




def    UpperCAmelCase					(  ):



			'''simple docstring'''





			a__					=  3
			a__					=  _create_spd_matrix(lowercase__	)
			a__					=  np.random.randn(lowercase__				,					1	)
			a__					=  np.dot(lowercase__				,					lowercase__	)

			# Numpy solution.
			a__					=  np.linalg.solve(lowercase__				,					lowercase__	)

			# Our implementation.
			a__					=  conjugate_gradient(lowercase__				,					lowercase__	)

			# Ensure both solutions are close to x_true (and therefore one another).
			assert np.linalg.norm(x_numpy - x_true	) <= 1e-6
			assert np.linalg.norm(x_conjugate_gradient - x_true	) <= 1e-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_conjugate_gradient()

