import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:					List[Any]						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Any						=					{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
SCREAMING_SNAKE_CASE__		:					Tuple						=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Dict     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			str ):







     '''simple docstring'''


     for attribute in key.split('.' ):
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  lowercase__ )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE__				:				str			=		getattr(lowercase__     ,  lowercase__ ).shape
     else:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		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":
          SCREAMING_SNAKE_CASE__				:				int			=		value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE__				:				Tuple			=		value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE__				:				Tuple			=		value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		value
     elif weight_type == "running_mean":
          SCREAMING_SNAKE_CASE__				:				Any			=		value
     elif weight_type == "running_var":
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		value
     elif weight_type == "num_batches_tracked":
          SCREAMING_SNAKE_CASE__				:				Tuple			=		value
     elif weight_type == "inv_freq":
          SCREAMING_SNAKE_CASE__				:				Dict			=		value
     else:
          SCREAMING_SNAKE_CASE__				:				Any			=		value

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







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







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Dict			=		[]
     SCREAMING_SNAKE_CASE__				:				List[str]			=		fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE__				:				int			=		hf_model.wavaveca_conformer.feature_extractor

     for name, value in fairseq_dict.items():
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		False
          if "conv_layers" in name:
               load_conv_layer(
                   lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
               SCREAMING_SNAKE_CASE__				:				Optional[int]			=		True
          else:
               for key, mapped_key in MAPPING.items():
                    SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'wav2vec2_conformer.' + 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]:
                         SCREAMING_SNAKE_CASE__				:				int			=		True
                         if "*" in mapped_key:
                              SCREAMING_SNAKE_CASE__				:				Tuple			=		name.split(lowercase__ )[0].split('.' )[-2]
                              SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		mapped_key.replace('*'     ,  lowercase__ )
                         if "pos_bias_u" in name:
                              SCREAMING_SNAKE_CASE__				:				List[Any]			=		None
                         elif "pos_bias_v" in name:
                              SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
                         elif "weight_g" in name:
                              SCREAMING_SNAKE_CASE__				:				Dict			=		'weight_g'
                         elif "weight_v" in name:
                              SCREAMING_SNAKE_CASE__				:				List[Any]			=		'weight_v'
                         elif "bias" in name:
                              SCREAMING_SNAKE_CASE__				:				Any			=		'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		'weight'
                         elif "running_mean" in name:
                              SCREAMING_SNAKE_CASE__				:				int			=		'running_mean'
                         elif "inv_freq" in name:
                              SCREAMING_SNAKE_CASE__				:				List[Any]			=		'inv_freq'
                         elif "running_var" in name:
                              SCREAMING_SNAKE_CASE__				:				str			=		'running_var'
                         elif "num_batches_tracked" in name:
                              SCREAMING_SNAKE_CASE__				:				List[Any]			=		'num_batches_tracked'
                         else:
                              SCREAMING_SNAKE_CASE__				:				Any			=		None
                         set_recursively(lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__     ,  lowercase__ )
                    continue
          if not is_used:
               unused_weights.append(lowercase__ )

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







def     _a				(      lowercase__  :			Union[str, Any]     ,  lowercase__  :			str     ,  lowercase__  :			Optional[Any]     ,  lowercase__  :			Union[str, Any]     ,  lowercase__  :			Union[str, Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		full_name.split('conv_layers.' )[-1]
     SCREAMING_SNAKE_CASE__				:				Dict			=		name.split('.' )
     SCREAMING_SNAKE_CASE__				:				List[str]			=		int(items[0] )
     SCREAMING_SNAKE_CASE__				:				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.''' )
               SCREAMING_SNAKE_CASE__				:				Union[str, 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.''' )
               SCREAMING_SNAKE_CASE__				:				Dict			=		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.''' )
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		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.''' )
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
     else:
          unused_weights.append(lowercase__ )







@torch.no_grad()
def     _a				(      lowercase__  :			List[Any]     ,  lowercase__  :			int     ,  lowercase__  :			List[Any]=None     ,  lowercase__  :			Tuple=None     ,  lowercase__  :			List[Any]=True ):







     '''simple docstring'''


     if config_path is not None:
          SCREAMING_SNAKE_CASE__				:				str			=		WavaVecaConformerConfig.from_pretrained(lowercase__     ,  hidden_act='swish' )
     else:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		WavaVecaConformerConfig()

     if "rope" in checkpoint_path:
          SCREAMING_SNAKE_CASE__				:				List[str]			=		'rotary'

     if is_finetuned:
          if dict_path:
               SCREAMING_SNAKE_CASE__				:				str			=		Dictionary.load(lowercase__ )

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

               # fairseq has the <pad> and <s> switched
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		0
               SCREAMING_SNAKE_CASE__				:				Any			=		1
               with open(lowercase__     ,  'w'     ,  encoding='utf-8' ) as vocab_handle:
                    json.dump(lowercase__     ,  lowercase__ )
               SCREAMING_SNAKE_CASE__				:				Dict			=		WavaVecaCTCTokenizer(
                   lowercase__     ,  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=lowercase__     ,  )
               SCREAMING_SNAKE_CASE__				:				List[str]			=		True if config.feat_extract_norm == 'layer' else False
               SCREAMING_SNAKE_CASE__				:				Dict			=		WavaVecaFeatureExtractor(
                   feature_size=1     ,  sampling_rate=1_60_00     ,  padding_value=0     ,  do_normalize=lowercase__     ,  return_attention_mask=lowercase__     ,  )
               SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		WavaVecaProcessor(feature_extractor=lowercase__     ,  tokenizer=lowercase__ )
               processor.save_pretrained(lowercase__ )

          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		WavaVecaConformerForCTC(lowercase__ )
     else:
          SCREAMING_SNAKE_CASE__				:				str			=		WavaVecaConformerForPreTraining(lowercase__ )

     if is_finetuned:
          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Tuple			=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
     else:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		argparse.Namespace(task='audio_pretraining' )
          SCREAMING_SNAKE_CASE__				:				List[str]			=		fairseq.tasks.setup_task(lowercase__ )

          SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[str]			=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowercase__ )

     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model[0].eval()

     recursively_load_weights(lowercase__     ,  lowercase__     ,  not is_finetuned )

     hf_wavavec.save_pretrained(lowercase__ )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:					Optional[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"
      )
      SCREAMING_SNAKE_CASE__		:					Optional[int]						=					parser.parse_args()
      convert_wavaveca_conformer_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



