




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__a          =			logging.get_logger(__name__)

__a          =			{
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     for attribute in key.split('''.'''				):
          UpperCAmelCase_       =      getattr(_lowercase			,						_lowercase				)

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_       =      []
     UpperCAmelCase_       =      fairseq_model.state_dict()

     UpperCAmelCase_       =      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_       =      False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						hf_model.config.feat_extract_norm == '''group'''			,						)
               UpperCAmelCase_       =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_       =      '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                    if key in name or (key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0] and not is_finetuned):
                         UpperCAmelCase_       =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_       =      name.split(_lowercase				)[0].split('''.'''				)[-2]
                              UpperCAmelCase_       =      mapped_key.replace('''*'''			,						_lowercase				)
                         if "weight_g" in name:
                              UpperCAmelCase_       =      '''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_       =      '''weight_v'''
                         elif "weight" in name:
                              UpperCAmelCase_       =      '''weight'''
                         elif "bias" in name:
                              UpperCAmelCase_       =      '''bias'''
                         else:
                              UpperCAmelCase_       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_       =      full_name.split('''conv_layers.'''				)[-1]
     UpperCAmelCase_       =      name.split('''.'''				)
     UpperCAmelCase_       =      int(items[0]				)
     UpperCAmelCase_       =      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.'''
               )
               UpperCAmelCase_       =      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.'''
               )
               UpperCAmelCase_       =      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."
               )
               UpperCAmelCase_       =      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.'''
               )
               UpperCAmelCase_       =      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 lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=None			,						_lowercase=None			,						_lowercase=True				):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_       =      HubertConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_       =      HubertConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_       =      Dictionary.load(_lowercase				)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCAmelCase_       =      target_dict.pad_index
               UpperCAmelCase_       =      target_dict.bos_index
               UpperCAmelCase_       =      target_dict.eos_index
               UpperCAmelCase_       =      len(target_dict.symbols				)
               UpperCAmelCase_       =      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				)
               with open(_lowercase			,						'''w'''			,						encoding='''utf-8'''				) as vocab_handle:
                    json.dump(target_dict.indices			,						_lowercase				)
               UpperCAmelCase_       =      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			,						)
               UpperCAmelCase_       =      True if config.feat_extract_norm == '''layer''' else False
               UpperCAmelCase_       =      WavaVecaFeatureExtractor(
                   feature_size=1			,						sampling_rate=16000			,						padding_value=0			,						do_normalize=_lowercase			,						return_attention_mask=_lowercase			,						)
               UpperCAmelCase_       =      WavaVecaProcessor(feature_extractor=_lowercase			,						tokenizer=_lowercase				)
               processor.save_pretrained(_lowercase				)

          UpperCAmelCase_       =      HubertForCTC(_lowercase				)
     else:
          UpperCAmelCase_       =      HubertModel(_lowercase				)

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

     UpperCAmelCase_       =      model[0].eval()

     recursively_load_weights(_lowercase			,						_lowercase			,						_lowercase				)

     hf_wavavec.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			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'
     )
     __a          =			parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

__a          =			logging.getLogger()







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							List[str]       =      argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     UpperCAmelCase_      :							Dict       =      parser.parse_args()
     return args.f



class   __a(					_a       ):






    """simple docstring"""





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							int       =      logging.StreamHandler(sys.stdout  )
         logger.addHandler(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							int       =      get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0       ,'''run_glue_deebert.py'''  )
              with patch.object(_SCREAMING_SNAKE_CASE       ,'''argv'''       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							List[str]       =      run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(_SCREAMING_SNAKE_CASE       ,0.6_66  )





    @slow
    @require_torch_non_multi_gpu
    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[Any]       =      '''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      '''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(_SCREAMING_SNAKE_CASE  )