



'''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()
lowerCamelCase							:List[str]				    =					logging.get_logger(__name__)

lowerCamelCase							: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''',
}
lowerCamelCase							:Optional[int]				    =					[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			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
												A_	:						Union[str, Any]	=     """lm_head"""

						A_	:						Optional[Any]	=     getattr(lowerCamelCase__						,      lowerCamelCase__    )

			if weight_type is not None:
						A_	:						str	=     getattr(lowerCamelCase__						,      lowerCamelCase__    ).shape
			else:
						A_	:						str	=     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_	:						Union[str, Any]	=     value
			elif weight_type == "weight_g":
						A_	:						Tuple	=     value
			elif weight_type == "weight_v":
						A_	:						Optional[Any]	=     value
			elif weight_type == "bias":
						A_	:						Tuple	=     value
			else:
						A_	:						List[str]	=     value

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						List[str]	=     []
			A_	:						List[Any]	=     fairseq_model.state_dict()

			A_	:						List[Any]	=     hf_model.unispeech.feature_extractor

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

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



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     full_name.split("""conv_layers."""    )[-1]
			A_	:						int	=     name.split("""."""    )
			A_	:						str	=     int(items[0]    )
			A_	:						int	=     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_	:						str	=     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_	:						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:
									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_	:						Optional[Any]	=     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_	:						str	=     value
									logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
			else:
						unused_weights.append(lowerCamelCase__    )



@torch.no_grad()
def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=None						,      lowerCamelCase__=None						,      lowerCamelCase__=True    ):
			'''simple docstring'''





			if config_path is not None:
						A_	:						Tuple	=     UniSpeechConfig.from_pretrained(lowerCamelCase__    )
			else:
						A_	:						Optional[int]	=     UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									A_	:						str	=     Dictionary.load_from_json(lowerCamelCase__    )

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									A_	:						Optional[int]	=     target_dict.pad_index
									A_	:						List[str]	=     target_dict.bos_index
									A_	:						str	=     target_dict.eos_index
									A_	:						str	=     len(target_dict.symbols    )
									A_	:						Optional[int]	=     os.path.join(lowerCamelCase__						,      """vocab.json"""    )
									if not os.path.isdir(lowerCamelCase__    ):
												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCamelCase__    )    )
												return
									os.makedirs(lowerCamelCase__						,      exist_ok=lowerCamelCase__    )
									A_	:						Any	=     target_dict.indices

									# fairseq has the <pad> and <s> switched
									A_	:						Tuple	=     42
									A_	:						Optional[Any]	=     43
									with open(lowerCamelCase__						,      """w"""						,      encoding="""utf-8"""    ) as vocab_handle:
												json.dump(lowerCamelCase__						,      lowerCamelCase__    )
									A_	:						int	=     WavaVecaPhonemeCTCTokenizer(
									    lowerCamelCase__						,      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=lowerCamelCase__						,      )
									A_	:						Optional[int]	=     True if config.feat_extract_norm == """layer""" else False
									A_	:						Any	=     WavaVecaFeatureExtractor(
									    feature_size=1						,      sampling_rate=1_60_00						,      padding_value=0						,      do_normalize=lowerCamelCase__						,      return_attention_mask=lowerCamelCase__						,      )
									A_	:						Any	=     WavaVecaProcessor(feature_extractor=lowerCamelCase__						,      tokenizer=lowerCamelCase__    )
									processor.save_pretrained(lowerCamelCase__    )

						A_	:						Union[str, Any]	=     UniSpeechForCTC(lowerCamelCase__    )
			else:
						A_	:						Optional[int]	=     UniSpeechForPreTraining(lowerCamelCase__    )

			if is_finetuned:
						A_,    A_,    A_	:						int	=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]						,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    ), """w2v_path""": checkpoint_path}    )
			else:
						A_,    A_,    A_	:						List[str]	=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

			A_	:						Tuple	=     model[0].eval()

			recursively_load_weights(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )

			hf_unispeech.save_pretrained(lowerCamelCase__    )


if __name__ == "__main__":
						lowerCamelCase							:str				    =					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'''
						)
						lowerCamelCase							:Optional[Any]				    =					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'''




# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowerCamelCase							:str				    =					TypeVar('''T''')







class 	_lowerCAmelCase							(			Generic[T] ):


	def __init__(self       ,      lowercase = True							):
				A_	:						dict[T, list[T]]	=     {}  # dictionary of lists
				A_	:						Any	=     directed


	def     _a	(self       ,      lowercase       ,      lowercase							):

				if not self.directed:  # For undirected graphs
							# if both source vertex and destination vertex are both present in the
							# adjacency list, add destination vertex to source vertex list of adjacent
							# vertices and add source vertex to destination vertex list of adjacent
							# vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										self.adj_list[destination_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination vertex
							# to source vertex list of adjacent vertices, then create a new vertex with
							# destination vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Dict	=     [source_vertex]
							# if only destination vertex is present in adjacency list, add source vertex
							# to destination vertex list of adjacent vertices, then create a new vertex
							# with source vertex as key and assign a list containing the source vertex
							# as it's first adjacent vertex.
							elif destination_vertex in self.adj_list:
										self.adj_list[destination_vertex].append(lowercase							)
										A_	:						int	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and assign a list
							# containing the destination vertex as it's first adjacent vertex also
							# create a new vertex with destination vertex as key and assign a list
							# containing the source vertex as it's first adjacent vertex.
							else:
										A_	:						Optional[Any]	=     [destination_vertex]
										A_	:						Tuple	=     [source_vertex]
				else:  # For directed graphs
							# if both source vertex and destination vertex are present in adjacency
							# list, add destination vertex to source vertex list of adjacent vertices.
							if source_vertex in self.adj_list and destination_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
							# if only source vertex is present in adjacency list, add destination
							# vertex to source vertex list of adjacent vertices and create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							elif source_vertex in self.adj_list:
										self.adj_list[source_vertex].append(lowercase							)
										A_	:						Tuple	=     []
							# if only destination vertex is present in adjacency list, create a new
							# vertex with source vertex as key and assign a list containing destination
							# vertex as first adjacent vertex
							elif destination_vertex in self.adj_list:
										A_	:						Tuple	=     [destination_vertex]
							# if both source vertex and destination vertex are not present in adjacency
							# list, create a new vertex with source vertex as key and a list containing
							# destination vertex as it's first adjacent vertex. Then create a new vertex
							# with destination vertex as key, which has no adjacent vertex
							else:
										A_	:						int	=     [destination_vertex]
										A_	:						List[str]	=     []

				return self


	def __repr__(self							):
				return pformat(self.adj_list							)