






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowercase    				=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 		__UpperCAmelCase     (	a_):
							snake_case_           =				{}
							with open(a_				,	'r') as file:
														for line_number, line in enumerate(a_):
																					snake_case_           =				line.strip()
																					if line:
																												snake_case_           =				line.split()
																												snake_case_           =				line_number
																												snake_case_           =				words[0]
																												snake_case_           =				value
							return result





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_):
							for attribute in key.split('.'):
														snake_case_           =				getattr(a_				,	a_)

							snake_case_           =				None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(a_):
																					snake_case_           =				PARAM_MAPPING[full_name.split('.')[-1]]
																					snake_case_           =				'param'

							if weight_type is not None and weight_type != "param":
														snake_case_           =				getattr(a_				,	a_).shape
							elif weight_type is not None and weight_type == "param":
														snake_case_           =				hf_pointer
														for attribute in hf_param_name.split('.'):
																					snake_case_           =				getattr(a_				,	a_)
														snake_case_           =				shape_pointer.shape

														# let's reduce dimension
														snake_case_           =				value[0]
							else:
														snake_case_           =				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":
														snake_case_           =				value
							elif weight_type == "weight_g":
														snake_case_           =				value
							elif weight_type == "weight_v":
														snake_case_           =				value
							elif weight_type == "bias":
														snake_case_           =				value
							elif weight_type == "param":
														for attribute in hf_param_name.split('.'):
																					snake_case_           =				getattr(a_				,	a_)
														snake_case_           =				value
							else:
														snake_case_           =				value

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





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_):
							snake_case_           =				None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(a_):
																					snake_case_           =				PARAM_MAPPING[full_name.split('.')[-1]]
																					snake_case_           =				'param'

							if weight_type is not None and weight_type != "param":
														snake_case_           =				'.'.join([key, weight_type])
							elif weight_type is not None and weight_type == "param":
														snake_case_           =				'.'.join([key, hf_param_name])
							else:
														snake_case_           =				key

							snake_case_           =				value if 'lm_head' in full_key else value[0]


lowercase    				=   {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 		__UpperCAmelCase     (	a_				,	a_				,	a_=None				,	a_=None):
							snake_case_           =				False
							for key, mapped_key in MAPPING.items():
														snake_case_           =				'wav2vec2.' + 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]:
																					snake_case_           =				True
																					if "*" in mapped_key:
																												snake_case_           =				name.split(a_)[0].split('.')[-2]
																												snake_case_           =				mapped_key.replace('*'				,	a_)
																					if "weight_g" in name:
																												snake_case_           =				'weight_g'
																					elif "weight_v" in name:
																												snake_case_           =				'weight_v'
																					elif "bias" in name:
																												snake_case_           =				'bias'
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												snake_case_           =				'weight'
																					else:
																												snake_case_           =				None
																					if hf_dict is not None:
																												rename_dict(a_				,	a_				,	a_				,	a_				,	a_)
																					else:
																												set_recursively(a_				,	a_				,	a_				,	a_				,	a_)
																					return is_used
							return is_used





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				[]
							snake_case_           =				fairseq_model.state_dict()

							snake_case_           =				hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														snake_case_           =				False
														if "conv_layers" in name:
																					load_conv_layer(
																					    a_				,	a_				,	a_				,	a_				,	hf_model.config.feat_extract_norm == 'group'				,	)
																					snake_case_           =				True
														else:
																					snake_case_           =				load_wavaveca_layer(a_				,	a_				,	a_)
														if not is_used:
																					unused_weights.append(a_)

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





def 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_				,	a_):
							snake_case_           =				full_name.split('conv_layers.')[-1]
							snake_case_           =				name.split('.')
							snake_case_           =				int(items[0])
							snake_case_           =				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.''')
																					snake_case_           =				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.''')
																					snake_case_           =				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.''')
																					snake_case_           =				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.''')
																					snake_case_           =				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_				,	a_				,	a_=None				,	a_=None				,	a_=True				,	a_=False):
							if config_path is not None:
														snake_case_           =				WavaVecaConfig.from_pretrained(a_)
							else:
														snake_case_           =				WavaVecaConfig()

							if is_seq_class:
														snake_case_           =				read_txt_into_dict(a_)
														snake_case_           =				idalabel
														snake_case_           =				WavaVecaForSequenceClassification(a_)
														snake_case_           =				WavaVecaFeatureExtractor(
														    feature_size=1				,	sampling_rate=1_60_00				,	padding_value=0				,	do_normalize=a_				,	return_attention_mask=a_				,	)
														feature_extractor.save_pretrained(a_)

							elif is_finetuned:
														if dict_path:
																					snake_case_           =				Dictionary.load(a_)

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

																					# fairseq has the <pad> and <s> switched
																					snake_case_           =				0
																					snake_case_           =				1
																					with open(a_				,	'w'				,	encoding='utf-8') as vocab_handle:
																												json.dump(a_				,	a_)
																					snake_case_           =				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_				,	)
																					snake_case_           =				True if config.feat_extract_norm == 'layer' else False
																					snake_case_           =				WavaVecaFeatureExtractor(
																					    feature_size=1				,	sampling_rate=1_60_00				,	padding_value=0				,	do_normalize=a_				,	return_attention_mask=a_				,	)
																					snake_case_           =				WavaVecaProcessor(feature_extractor=a_				,	tokenizer=a_)
																					processor.save_pretrained(a_)

														snake_case_           =				WavaVecaForCTC(a_)
							else:
														snake_case_           =				WavaVecaForPreTraining(a_)

							if is_finetuned or is_seq_class:
														snake_case_     ,						snake_case_     ,						snake_case_           =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]				,	arg_overrides={'data': '/'.join(dict_path.split('/')[:-1])})
							else:
														snake_case_           =				argparse.Namespace(task='audio_pretraining')
														snake_case_           =				fairseq.tasks.setup_task(a_)

														snake_case_     ,						snake_case_     ,						snake_case_           =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,	task=a_)

							snake_case_           =				model[0].eval()

							recursively_load_weights(a_				,	a_				,	not is_finetuned)

							hf_wavavec.save_pretrained(a_)


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"
	)
	parser.add_argument(
	    "--is_seq_class",
	    action="store_true",
	    help="Whether the model to convert is a fine-tuned sequence classification model or not",
	)
	lowercase    				=   parser.parse_args()

	lowercase    				=   not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)













from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")




class 					UpperCamelCase_ (     Generic[T]							):



	'''simple docstring'''




	lowerCAmelCase						=					42  # Cache store of keys
	lowerCAmelCase						=					42  # References of the keys in cache
	lowerCAmelCase						=					1_0  # Maximum capacity of cache




	def __init__(			self			,     a   )			->       None:
								snake_case_           =				deque()
								snake_case_           =				set()
								if not n:
															snake_case_           =				sys.maxsize
								elif n < 0:
															raise ValueError('n should be an integer greater than 0.'   )
								else:
															snake_case_           =				n




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								if x not in self.key_reference:
															if len(self.dq_store   ) == LRUCache._MAX_CAPACITY:
																						snake_case_           =				self.dq_store.pop()
																						self.key_reference.remove(a   )
								else:
															self.dq_store.remove(a   )

								self.dq_store.appendleft(a   )
								self.key_reference.add(a   )




	def 				_UpperCamelCase     (			self   )			->       None:
								for k in self.dq_store:
															print(a   )






	def __repr__(			self   )			->       str:
								return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store   )}'''


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	lowercase    				=   LRUCache(4)
	lru_cache.refer("A")
	lru_cache.refer(2)
	lru_cache.refer(3)
	lru_cache.refer("A")
	lru_cache.refer(4)
	lru_cache.refer(5)
	lru_cache.display()

	print(lru_cache)
	assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"





