






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()
__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    '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',
}
__UpperCAmelCase								=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def      lowercase__      (     __snake_case    :							List[str]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						{}
					with open(__UpperCAmelCase     ,       'r'  ) as file:
										for line_number, line in enumerate(__UpperCAmelCase  ):
															UpperCAmelCase_    :      Dict            =						line.strip()
															if line:
																				UpperCAmelCase_    :      str            =						line.split()
																				UpperCAmelCase_    :      Optional[int]            =						line_number
																				UpperCAmelCase_    :      Optional[Any]            =						words[0]
																				UpperCAmelCase_    :      str            =						value
					return result




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							Tuple     ,       __snake_case    :							List[Any]     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							Optional[int]  ):


					'''simple docstring'''


					for attribute in key.split('.'  ):
										UpperCAmelCase_    :      List[str]            =						getattr(__UpperCAmelCase     ,       __UpperCAmelCase  )

					UpperCAmelCase_    :      List[str]            =						None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(__UpperCAmelCase  ):
															UpperCAmelCase_    :      Optional[Any]            =						PARAM_MAPPING[full_name.split('.'  )[-1]]
															UpperCAmelCase_    :      str            =						'param'

					if weight_type is not None and weight_type != "param":
										UpperCAmelCase_    :      List[str]            =						getattr(__UpperCAmelCase     ,       __UpperCAmelCase  ).shape
					elif weight_type is not None and weight_type == "param":
										UpperCAmelCase_    :      int            =						hf_pointer
										for attribute in hf_param_name.split('.'  ):
															UpperCAmelCase_    :      Tuple            =						getattr(__UpperCAmelCase     ,       __UpperCAmelCase  )
										UpperCAmelCase_    :      Dict            =						shape_pointer.shape

										# let's reduce dimension
										UpperCAmelCase_    :      Any            =						value[0]
					else:
										UpperCAmelCase_    :      List[Any]            =						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":
										UpperCAmelCase_    :      Dict            =						value
					elif weight_type == "weight_g":
										UpperCAmelCase_    :      Dict            =						value
					elif weight_type == "weight_v":
										UpperCAmelCase_    :      Optional[int]            =						value
					elif weight_type == "bias":
										UpperCAmelCase_    :      Dict            =						value
					elif weight_type == "param":
										for attribute in hf_param_name.split('.'  ):
															UpperCAmelCase_    :      Any            =						getattr(__UpperCAmelCase     ,       __UpperCAmelCase  )
										UpperCAmelCase_    :      int            =						value
					else:
										UpperCAmelCase_    :      List[str]            =						value

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




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Dict     ,       __snake_case    :							List[str]     ,       __snake_case    :							List[Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						None
					for param_key in PARAM_MAPPING.keys():
										if full_name.endswith(__UpperCAmelCase  ):
															UpperCAmelCase_    :      Dict            =						PARAM_MAPPING[full_name.split('.'  )[-1]]
															UpperCAmelCase_    :      Optional[Any]            =						'param'

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

					UpperCAmelCase_    :      Tuple            =						value if 'lm_head' in full_key else value[0]


__UpperCAmelCase								=						{
    '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      lowercase__      (     __snake_case    :							List[Any]     ,       __snake_case    :							Dict     ,       __snake_case    :							str=None     ,       __snake_case    :							str=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						False
					for key, mapped_key in MAPPING.items():
										UpperCAmelCase_    :      Optional[int]            =						'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]:
															UpperCAmelCase_    :      Any            =						True
															if "*" in mapped_key:
																				UpperCAmelCase_    :      Dict            =						name.split(__UpperCAmelCase  )[0].split('.'  )[-2]
																				UpperCAmelCase_    :      str            =						mapped_key.replace('*'     ,       __UpperCAmelCase  )
															if "weight_g" in name:
																				UpperCAmelCase_    :      List[Any]            =						'weight_g'
															elif "weight_v" in name:
																				UpperCAmelCase_    :      int            =						'weight_v'
															elif "bias" in name:
																				UpperCAmelCase_    :      str            =						'bias'
															elif "weight" in name:
																				# TODO: don't match quantizer.weight_proj
																				UpperCAmelCase_    :      Tuple            =						'weight'
															else:
																				UpperCAmelCase_    :      List[Any]            =						None
															if hf_dict is not None:
																				rename_dict(__UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase  )
															else:
																				set_recursively(__UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase  )
															return is_used
					return is_used




def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							List[str]     ,       __snake_case    :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Union[str, Any]            =						[]
					UpperCAmelCase_    :      Any            =						fairseq_model.state_dict()

					UpperCAmelCase_    :      Tuple            =						hf_model.wavaveca.feature_extractor

					for name, value in fairseq_dict.items():
										UpperCAmelCase_    :      Dict            =						False
										if "conv_layers" in name:
															load_conv_layer(
															    __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase     ,       hf_model.config.feat_extract_norm == 'group'     ,       )
															UpperCAmelCase_    :      Optional[Any]            =						True
										else:
															UpperCAmelCase_    :      List[str]            =						load_wavaveca_layer(__UpperCAmelCase     ,       __UpperCAmelCase     ,       __UpperCAmelCase  )
										if not is_used:
															unused_weights.append(__UpperCAmelCase  )

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




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							Dict     ,       __snake_case    :							List[str]  ):


					'''simple docstring'''


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




@torch.no_grad()
def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							List[Any]     ,       __snake_case    :							Any=None     ,       __snake_case    :							Optional[Any]=None     ,       __snake_case    :							List[Any]=True     ,       __snake_case    :							List[str]=False  ):


					'''simple docstring'''


					if config_path is not None:
										UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaConfig.from_pretrained(__UpperCAmelCase  )
					else:
										UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaConfig()

					if is_seq_class:
										UpperCAmelCase_    :      Optional[int]            =						read_txt_into_dict(__UpperCAmelCase  )
										UpperCAmelCase_    :      str            =						idalabel
										UpperCAmelCase_    :      Dict            =						WavaVecaForSequenceClassification(__UpperCAmelCase  )
										UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaFeatureExtractor(
										    feature_size=1     ,       sampling_rate=16_000     ,       padding_value=0     ,       do_normalize=__UpperCAmelCase     ,       return_attention_mask=__UpperCAmelCase     ,       )
										feature_extractor.save_pretrained(__UpperCAmelCase  )

					elif is_finetuned:
										if dict_path:
															UpperCAmelCase_    :      Dict            =						Dictionary.load(__UpperCAmelCase  )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															UpperCAmelCase_    :      int            =						target_dict.pad_index
															UpperCAmelCase_    :      Optional[Any]            =						target_dict.bos_index
															UpperCAmelCase_    :      int            =						target_dict.eos_index
															UpperCAmelCase_    :      List[Any]            =						len(target_dict.symbols  )
															UpperCAmelCase_    :      Any            =						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  )
															UpperCAmelCase_    :      Optional[Any]            =						target_dict.indices

															# fairseq has the <pad> and <s> switched
															UpperCAmelCase_    :      Tuple            =						0
															UpperCAmelCase_    :      Any            =						1
															with open(__UpperCAmelCase     ,       'w'     ,       encoding='utf-8'  ) as vocab_handle:
																				json.dump(__UpperCAmelCase     ,       __UpperCAmelCase  )
															UpperCAmelCase_    :      Tuple            =						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_    :      Tuple            =						True if config.feat_extract_norm == 'layer' else False
															UpperCAmelCase_    :      Optional[Any]            =						WavaVecaFeatureExtractor(
															    feature_size=1     ,       sampling_rate=16_000     ,       padding_value=0     ,       do_normalize=__UpperCAmelCase     ,       return_attention_mask=__UpperCAmelCase     ,       )
															UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaProcessor(feature_extractor=__UpperCAmelCase     ,       tokenizer=__UpperCAmelCase  )
															processor.save_pretrained(__UpperCAmelCase  )

										UpperCAmelCase_    :      Optional[int]            =						WavaVecaForCTC(__UpperCAmelCase  )
					else:
										UpperCAmelCase_    :      Union[str, Any]            =						WavaVecaForPreTraining(__UpperCAmelCase  )

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

										UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,       task=__UpperCAmelCase  )

					UpperCAmelCase_    :      str            =						model[0].eval()

					recursively_load_weights(__UpperCAmelCase     ,       __UpperCAmelCase     ,       not is_finetuned  )

					hf_wavavec.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(
		    '--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',
		)
		__UpperCAmelCase								=						parser.parse_args()

		__UpperCAmelCase								=						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

lowerCamelCase__ :   Optional[int]					=							[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCamelCase__ :   List[Any]					=							[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)

      for i in range(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for j in range(i + 1			,			__UpperCAmelCase							):
                  if arr[i] < arr[j]:
                        SCREAMING_SNAKE_CASE_							=			arr[j]
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			[]
      for i, outer in enumerate(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_							=			-1
            for inner in arr[i + 1 :]:
                  if outer < inner:
                        SCREAMING_SNAKE_CASE_							=			inner
                        break
            result.append(__UpperCAmelCase							)
      return result

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	list[float]							)					-> list[float]:
      SCREAMING_SNAKE_CASE_							=			len(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			[-1] * arr_size

      for index in reversed(range(__UpperCAmelCase							)							):
            if stack:
                  while stack[-1] <= arr[index]:
                        stack.pop()
                        if not stack:
                              break
            if stack:
                  SCREAMING_SNAKE_CASE_							=			stack[-1]
            stack.append(arr[index]							)
      return result


if __name__ == "__main__":
   from doctest import testmod
   from timeit import timeit

   testmod()
   print(next_greatest_element_slow(arr))
   print(next_greatest_element_fast(arr))
   print(next_greatest_element(arr))

   lowerCamelCase__ :   List[str]					=							(
       'from __main__ import arr, next_greatest_element_slow, '
       'next_greatest_element_fast, next_greatest_element'
   )
   print(
       'next_greatest_element_slow():',
       timeit('next_greatest_element_slow(arr)', setup=setup),
   )
   print(
       'next_greatest_element_fast():',
       timeit('next_greatest_element_fast(arr)', setup=setup),
   )
   print(
       '     next_greatest_element():',
       timeit('next_greatest_element(arr)', setup=setup),
   )