



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_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''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_													=      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     List[Any]						)	->    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
																				__lowerCAmelCase						 ="""lm_head"""

										__lowerCAmelCase						 =getattr(__lowerCamelCase					, __lowerCamelCase						)

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

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






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Union[str, Any]						)	->    Union[str, Any]:
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =fairseq_model.state_dict()

					__lowerCAmelCase						 =hf_model.unispeech.feature_extractor

					for name, value in fairseq_dict.items():
										__lowerCAmelCase						 =False
										if "conv_layers" in name:
															load_conv_layer(
															    __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, hf_model.config.feat_extract_norm == """group"""					, )
															__lowerCAmelCase						 =True
										else:
															for key, mapped_key in MAPPING.items():
																				__lowerCAmelCase						 ="""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]:
																									__lowerCAmelCase						 =True
																									if "*" in mapped_key:
																														__lowerCAmelCase						 =name.split(__lowerCamelCase						)[0].split("""."""						)[-2]
																														__lowerCAmelCase						 =mapped_key.replace("""*"""					, __lowerCamelCase						)
																									if "weight_g" in name:
																														__lowerCAmelCase						 ="""weight_g"""
																									elif "weight_v" in name:
																														__lowerCAmelCase						 ="""weight_v"""
																									elif "bias" in name:
																														__lowerCAmelCase						 ="""bias"""
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														__lowerCAmelCase						 ="""weight"""
																									else:
																														__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    int:
					__lowerCAmelCase						 =full_name.split("""conv_layers."""						)[-1]
					__lowerCAmelCase						 =name.split("""."""						)
					__lowerCAmelCase						 =int(items[0]						)
					__lowerCAmelCase						 =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."""
															)
															__lowerCAmelCase						 =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."""
															)
															__lowerCAmelCase						 =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."
															)
															__lowerCAmelCase						 =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."""
															)
															__lowerCAmelCase						 =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        __lowerCAmelCase		(	__lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Optional[Any]=None					, __lowerCamelCase						:     Union[str, Any]=None					, __lowerCamelCase						:     Any=True						)	->    List[Any]:
					if config_path is not None:
										__lowerCAmelCase						 =UniSpeechConfig.from_pretrained(__lowerCamelCase						)
					else:
										__lowerCAmelCase						 =UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															__lowerCAmelCase						 =Dictionary.load_from_json(__lowerCamelCase						)

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__lowerCAmelCase						 =target_dict.pad_index
															__lowerCAmelCase						 =target_dict.bos_index
															__lowerCAmelCase						 =target_dict.eos_index
															__lowerCAmelCase						 =len(target_dict.symbols						)
															__lowerCAmelCase						 =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						)
															__lowerCAmelCase						 =target_dict.indices

															# fairseq has the <pad> and <s> switched
															__lowerCAmelCase						 =42
															__lowerCAmelCase						 =43
															with open(__lowerCamelCase					, """w"""					, encoding="""utf-8"""						) as vocab_handle:
																				json.dump(__lowerCamelCase					, __lowerCamelCase						)
															__lowerCAmelCase						 =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					, )
															__lowerCAmelCase						 =True if config.feat_extract_norm == """layer""" else False
															__lowerCAmelCase						 =WavaVecaFeatureExtractor(
															    feature_size=1					, sampling_rate=16000					, padding_value=0					, do_normalize=__lowerCamelCase					, return_attention_mask=__lowerCamelCase					, )
															__lowerCAmelCase						 =WavaVecaProcessor(feature_extractor=__lowerCamelCase					, tokenizer=__lowerCamelCase						)
															processor.save_pretrained(__lowerCamelCase						)

										__lowerCAmelCase						 =UniSpeechForCTC(__lowerCamelCase						)
					else:
										__lowerCAmelCase						 =UniSpeechForPreTraining(__lowerCamelCase						)

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

					__lowerCAmelCase						 =model[0].eval()

					recursively_load_weights(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)

					hf_unispeech.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
				lowercase_													=      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_													=      parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)









from collections.abc import Sequence
from queue import Queue






class     __a			:



	def __init__(    self				:     str					,						snake_case_				:     List[str]					,						snake_case_				:     Tuple					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[Any]=None					,						snake_case_				:     Dict=None)->				Optional[int]:
						__lowerCAmelCase						 =start
						__lowerCAmelCase						 =end
						__lowerCAmelCase						 =val
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =left
						__lowerCAmelCase						 =right



	def __repr__(    self				:     List[Any])->				Dict:
						return F"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""






class     __a			:



	def __init__(    self				:     List[str]					,						snake_case_				:     Sequence					,						snake_case_				:     int)->				str:
						__lowerCAmelCase						 =collection
						__lowerCAmelCase						 =function
						if self.collection:
											__lowerCAmelCase						 =self._build_tree(0					,						len(snake_case_) - 1)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     Optional[int])->				Optional[int]:
						self._update_tree(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Optional[Any]					,						snake_case_				:     Any					,						snake_case_				:     Union[str, Any])->				Union[str, Any]:
						return self._query_range(self.root					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     List[str]					,						snake_case_				:     List[str])->				List[Any]:
						if start == end:
											return SegmentTreeNode(snake_case_					,						snake_case_					,						self.collection[start])
						__lowerCAmelCase						 =(start + end) // 2
						__lowerCAmelCase						 =self._build_tree(snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self._build_tree(mid + 1					,						snake_case_)
						return SegmentTreeNode(snake_case_					,						snake_case_					,						self.fn(left.val					,						right.val)					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     Dict					,						snake_case_				:     Tuple					,						snake_case_				:     int					,						snake_case_				:     int)->				List[Any]:
						if node.start == i and node.end == i:
											__lowerCAmelCase						 =val
											return
						if i <= node.mid:
											self._update_tree(node.left					,						snake_case_					,						snake_case_)
						else:
											self._update_tree(node.right					,						snake_case_					,						snake_case_)
						__lowerCAmelCase						 =self.fn(node.left.val					,						node.right.val)



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     str					,						snake_case_				:     List[Any])->				Optional[int]:
						if node.start == i and node.end == j:
											return node.val

						if i <= node.mid:
											if j <= node.mid:
																# range in left child tree
																return self._query_range(node.left					,						snake_case_					,						snake_case_)
											else:
																# range in left child tree and right child tree
																return self.fn(
																    self._query_range(node.left					,						snake_case_					,						node.mid)					,						self._query_range(node.right					,						node.mid + 1					,						snake_case_)					,						)
						else:
											# range in right child tree
											return self._query_range(node.right					,						snake_case_					,						snake_case_)



	def 		UpperCamelCase				(    self				:     List[str])->				Tuple:
						if self.root is not None:
											__lowerCAmelCase						 =Queue()
											queue.put(self.root)
											while not queue.empty():
																__lowerCAmelCase						 =queue.get()
																yield node

																if node.left is not None:
																					queue.put(node.left)

																if node.right is not None:
																					queue.put(node.right)


if __name__ == "__main__":
				import operator

				for fn in [operator.add, max, min]:
								print('''*''' * 50)
								lowercase_													=      SegmentTree([2, 1, 5, 3, 4], fn)
								for node in arr.traverse():
												print(node)
								print()

								arr.update(1, 5)
								for node in arr.traverse():
												print(node)
								print()

								print(arr.query_range(3, 4))  # 7
								print(arr.query_range(2, 2))  # 5
								print(arr.query_range(1, 3))  # 13
								print()




