

"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase   :	Union[str, Any]												=						logging.get_logger(__name__)

_lowercase   :	Optional[int]												=						{
    'post_extract_proj': 'feature_projection.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.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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
_lowercase   :	List[Any]												=						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def       lowercase__						(       snake_case_   :Any			,		snake_case_   :Union[str, Any]			,		snake_case_   :str			,		snake_case_   :Tuple			,		snake_case_   :Optional[int]			,		snake_case_   :List[str]    ):
	for attribute in key.split('''.'''    ):
		if is_finetuned:
			if attribute in ["quantizer", "project_q", "project_hid"]:
				# those layers are only relevant for pretraining and should be dropped
				return

			if attribute == "ctc_proj":
				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
				__UpperCAmelCase      		= '''lm_head'''

		__UpperCAmelCase      		= getattr(snake_case_			,		snake_case_    )

	if weight_type is not None:
		__UpperCAmelCase      		= getattr(snake_case_			,		snake_case_    ).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       lowercase__						(       snake_case_   :str			,		snake_case_   :Tuple			,		snake_case_   :Optional[Any]    ):
	__UpperCAmelCase      		= []
	__UpperCAmelCase      		= fairseq_model.state_dict()

	__UpperCAmelCase      		= hf_model.unispeech.feature_extractor

	for name, value in fairseq_dict.items():
		__UpperCAmelCase      		= False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		hf_model.config.feat_extract_norm == '''group'''			,		)
			__UpperCAmelCase      		= True
		else:
			for key, mapped_key in MAPPING.items():
				__UpperCAmelCase      		= '''unispeech.''' + 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]:
					__UpperCAmelCase      		= True
					if "*" in mapped_key:
						__UpperCAmelCase      		= name.split(snake_case_    )[0].split('''.'''    )[-2]
						__UpperCAmelCase      		= mapped_key.replace('''*'''			,		snake_case_    )
					if "weight_g" in name:
						__UpperCAmelCase      		= '''weight_g'''
					elif "weight_v" in name:
						__UpperCAmelCase      		= '''weight_v'''
					elif "bias" in name:
						__UpperCAmelCase      		= '''bias'''
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						__UpperCAmelCase      		= '''weight'''
					else:
						__UpperCAmelCase      		= None
					set_recursively(snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_    )
				continue
		if not is_used:
			unused_weights.append(snake_case_    )

	logger.warning(F'''Unused weights: {unused_weights}'''    )





def       lowercase__						(       snake_case_   :Union[str, Any]			,		snake_case_   :Union[str, Any]			,		snake_case_   :Optional[int]			,		snake_case_   :int			,		snake_case_   :List[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(snake_case_    )





@torch.no_grad()
def       lowercase__						(       snake_case_   :List[str]			,		snake_case_   :Optional[int]			,		snake_case_   :List[str]=None			,		snake_case_   :Optional[Any]=None			,		snake_case_   :Optional[int]=True    ):
	if config_path is not None:
		__UpperCAmelCase      		= UniSpeechConfig.from_pretrained(snake_case_    )
	else:
		__UpperCAmelCase      		= UniSpeechConfig()

	if is_finetuned:
		if dict_path:
			__UpperCAmelCase      		= Dictionary.load_from_json(snake_case_    )

			# 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.eos_index
			__UpperCAmelCase      		= len(target_dict.symbols    )
			__UpperCAmelCase      		= os.path.join(snake_case_			,		'''vocab.json'''    )
			if not os.path.isdir(snake_case_    ):
				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case_    )    )
				return
			os.makedirs(snake_case_			,		exist_ok=snake_case_    )
			__UpperCAmelCase      		= target_dict.indices

			# fairseq has the <pad> and <s> switched
			__UpperCAmelCase      		= 42
			__UpperCAmelCase      		= 43
			with open(snake_case_			,		'''w'''			,		encoding='''utf-8'''    ) as vocab_handle:
				json.dump(snake_case_			,		snake_case_    )
			__UpperCAmelCase      		= WavaVecaPhonemeCTCTokenizer(
			    snake_case_			,		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=snake_case_			,		)
			__UpperCAmelCase      		= True if config.feat_extract_norm == '''layer''' else False
			__UpperCAmelCase      		= WavaVecaFeatureExtractor(
			    feature_size=1			,		sampling_rate=16_000			,		padding_value=0			,		do_normalize=snake_case_			,		return_attention_mask=snake_case_			,		)
			__UpperCAmelCase      		= WavaVecaProcessor(feature_extractor=snake_case_			,		tokenizer=snake_case_    )
			processor.save_pretrained(snake_case_    )

		__UpperCAmelCase      		= UniSpeechForCTC(snake_case_    )
	else:
		__UpperCAmelCase      		= UniSpeechForPreTraining(snake_case_    )

	if is_finetuned:
		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]			,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    ), '''w2v_path''': checkpoint_path}    )
	else:
		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

	__UpperCAmelCase      		= model[0].eval()

	recursively_load_weights(snake_case_			,		snake_case_			,		snake_case_    )

	hf_unispeech.save_pretrained(snake_case_    )


if __name__ == "__main__":
				_lowercase   :	Any												=						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'
				)
				_lowercase   :	List[str]												=						parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)

"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




