




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_snake_case      =			{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def 							lowerCAmelCase_      (	snake_case_,snake_case_,snake_case_,snake_case_,snake_case_	):
      for attribute in key.split("""."""	):
            _A				:	str     =		getattr(snake_case_,snake_case_	)

      if weight_type is not None:
            _A				:	str     =		getattr(snake_case_,snake_case_	).shape
      else:
            _A				:	List[Any]     =		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":
            _A				:	Dict     =		value
      elif weight_type == "weight_g":
            _A				:	List[str]     =		value
      elif weight_type == "weight_v":
            _A				:	Optional[Any]     =		value
      elif weight_type == "bias":
            _A				:	List[str]     =		value
      else:
            _A				:	Optional[Any]     =		value

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







def 							lowerCAmelCase_      (	snake_case_,snake_case_,snake_case_	):
      _A				:	Dict     =		[]
      _A				:	Tuple     =		fairseq_model.state_dict()

      _A				:	Union[str, Any]     =		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            _A				:	Dict     =		False
            if "conv_layers" in name:
                  load_conv_layer(
                      snake_case_,snake_case_,snake_case_,snake_case_,hf_model.config.feat_extract_norm == """group""",)
                  _A				:	Optional[Any]     =		True
            else:
                  for key, mapped_key in MAPPING.items():
                        _A				:	List[str]     =		"""sew.""" + 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]:
                              _A				:	Any     =		True
                              if "*" in mapped_key:
                                    _A				:	str     =		name.split(snake_case_	)[0].split("""."""	)[-2]
                                    _A				:	Any     =		mapped_key.replace("""*""",snake_case_	)
                              if "weight_g" in name:
                                    _A				:	List[str]     =		"""weight_g"""
                              elif "weight_v" in name:
                                    _A				:	str     =		"""weight_v"""
                              elif "weight" in name:
                                    _A				:	Tuple     =		"""weight"""
                              elif "bias" in name:
                                    _A				:	Dict     =		"""bias"""
                              else:
                                    _A				:	Any     =		None
                              set_recursively(snake_case_,snake_case_,snake_case_,snake_case_,snake_case_	)
                        continue
            if not is_used:
                  unused_weights.append(snake_case_	)

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







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







def 							lowerCAmelCase_      (	snake_case_,snake_case_	):
      _A				:	Tuple     =		SEWConfig()
      if is_finetuned:
            _A				:	Optional[Any]     =		model.wav_encoder.wav_model.cfg
      else:
            _A				:	str     =		model.cfg

      _A				:	Optional[Any]     =		fs_config.conv_bias
      _A				:	Tuple     =		eval(fs_config.conv_feature_layers	)
      _A				:	List[str]     =		[x[0] for x in conv_layers]
      _A				:	Tuple     =		[x[1] for x in conv_layers]
      _A				:	int     =		[x[2] for x in conv_layers]
      _A				:	List[Any]     =		"""gelu"""
      _A				:	List[str]     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
      _A				:	Optional[Any]     =		0.0
      _A				:	str     =		fs_config.activation_fn.name
      _A				:	Optional[Any]     =		fs_config.encoder_embed_dim
      _A				:	Union[str, Any]     =		0.02
      _A				:	str     =		fs_config.encoder_ffn_embed_dim
      _A				:	List[Any]     =		1e-5
      _A				:	Dict     =		fs_config.encoder_layerdrop
      _A				:	int     =		fs_config.encoder_attention_heads
      _A				:	Union[str, Any]     =		fs_config.conv_pos_groups
      _A				:	Any     =		fs_config.conv_pos
      _A				:	str     =		len(snake_case_	)
      _A				:	List[Any]     =		fs_config.encoder_layers
      _A				:	Optional[Any]     =		fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            _A				:	Union[str, Any]     =		model.cfg
            _A				:	Optional[Any]     =		fs_config.final_dropout
            _A				:	Union[str, Any]     =		fs_config.layerdrop
      _A				:	Union[str, Any]     =		fs_config.activation_dropout
      _A				:	List[str]     =		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _A				:	List[str]     =		fs_config.attention_dropout
      _A				:	Tuple     =		fs_config.dropout_input
      _A				:	Optional[Any]     =		fs_config.dropout
      _A				:	List[str]     =		fs_config.mask_channel_length
      _A				:	str     =		fs_config.mask_channel_prob
      _A				:	List[Any]     =		fs_config.mask_length
      _A				:	List[Any]     =		fs_config.mask_prob

      _A				:	List[str]     =		"""Wav2Vec2FeatureExtractor"""
      _A				:	str     =		"""Wav2Vec2CTCTokenizer"""

      return config







@torch.no_grad()
def 							lowerCAmelCase_      (	snake_case_,snake_case_,snake_case_=None,snake_case_=None,snake_case_=True	):

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

      if config_path is not None:
            _A				:	Tuple     =		SEWConfig.from_pretrained(snake_case_	)
      else:
            _A				:	int     =		convert_config(model[0],snake_case_	)
      _A				:	str     =		model[0].eval()

      _A				:	Tuple     =		True if config.feat_extract_norm == """layer""" else False
      _A				:	Optional[int]     =		WavaVecaFeatureExtractor(
          feature_size=1,sampling_rate=16000,padding_value=0,do_normalize=snake_case_,return_attention_mask=snake_case_,)

      if is_finetuned:
            if dict_path:
                  _A				:	List[str]     =		Dictionary.load(snake_case_	)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  _A				:	Dict     =		target_dict.pad_index
                  _A				:	List[str]     =		target_dict.bos_index
                  _A				:	List[Any]     =		target_dict.pad_index
                  _A				:	Tuple     =		target_dict.bos_index
                  _A				:	Optional[Any]     =		target_dict.eos_index
                  _A				:	Union[str, Any]     =		len(target_dict.symbols	)
                  _A				:	int     =		os.path.join(snake_case_,"""vocab.json"""	)
                  if not os.path.isdir(snake_case_	):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case_	)	)
                        return
                  os.makedirs(snake_case_,exist_ok=snake_case_	)
                  with open(snake_case_,"""w""",encoding="""utf-8"""	) as vocab_handle:
                        json.dump(target_dict.indices,snake_case_	)
                  _A				:	Tuple     =		WavaVecaCTCTokenizer(
                      snake_case_,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=snake_case_,)
                  _A				:	int     =		WavaVecaProcessor(feature_extractor=snake_case_,tokenizer=snake_case_	)
                  processor.save_pretrained(snake_case_	)

            _A				:	Dict     =		SEWForCTC(snake_case_	)
      else:
            _A				:	Optional[int]     =		SEWModel(snake_case_	)
            feature_extractor.save_pretrained(snake_case_	)

      recursively_load_weights(snake_case_,snake_case_,snake_case_	)

      hf_model.save_pretrained(snake_case_	)


if __name__ == "__main__":
  _snake_case      =			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(
      "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  _snake_case      =			parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_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__ (snake_case   :		List[Any]				)     ->						Optional[Any]:
					return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)





def 				A__ (snake_case   :		int				)     ->						Any:
					__UpperCamelCase					:	List[str]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	List[Any]             =      gather(snake_case				)
					assert gathered_tensor.tolist() == list(range(1		,	state.num_processes**2 + 1				)				)





def 				A__ (snake_case   :		Optional[Any]				)     ->						Dict:
					__UpperCamelCase					:	Any             =      [state.process_index]
					__UpperCamelCase					:	int             =      gather_object(snake_case				)
					assert len(snake_case				) == state.num_processes, F'''{gathered_obj}, {len(snake_case				)} != {state.num_processes}'''
					assert gathered_obj == list(range(state.num_processes				)				), F'''{gathered_obj} != {list(range(state.num_processes				)				)}'''





def 				A__ (snake_case   :		Any				)     ->						Optional[Any]:
					__UpperCamelCase					:	Optional[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	Any             =      broadcast(snake_case				)
					assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
					assert broadcasted_tensor.tolist() == list(range(1		,	state.num_processes + 1				)				)





def 				A__ (snake_case   :		Tuple				)     ->						Dict:
					# We need to pad the tensor with one more element if we are the main process
					# to ensure that we can pad
					if state.is_main_process:
										__UpperCamelCase					:	List[Any]             =      torch.arange(state.num_processes + 1				).to(state.device				)
					else:
										__UpperCamelCase					:	List[str]             =      torch.arange(state.num_processes				).to(state.device				)
					__UpperCamelCase					:	List[str]             =      pad_across_processes(snake_case				)
					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__ (snake_case   :		Tuple				)     ->						Dict:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	Optional[int]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""sum"""				)
					__UpperCamelCase					:	Optional[int]             =      torch.tensor([4.0, 6]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Optional[Any]				)     ->						Optional[Any]:
					# For now runs on only two processes
					if state.num_processes != 2:
										return
					__UpperCamelCase					:	List[Any]             =      create_tensor(snake_case				)
					__UpperCamelCase					:	str             =      reduce(snake_case		,	"""mean"""				)
					__UpperCamelCase					:	Union[str, Any]             =      torch.tensor([2.0, 3]				).to(state.device				)
					assert torch.allclose(snake_case		,	snake_case				), F'''{reduced_tensor} != {truth_tensor}'''





def 				A__ (snake_case   :		Tuple				)     ->						int:
					# For xla_spawn (TPUs)
					main()





def 				A__ ()     ->						List[Any]:
					__UpperCamelCase					:	List[Any]             =      PartialState()
					state.print(F'''State: {state}'''				)
					state.print("""testing gather"""				)
					test_gather(snake_case				)
					state.print("""testing gather_object"""				)
					test_gather_object(snake_case				)
					state.print("""testing broadcast"""				)
					test_broadcast(snake_case				)
					state.print("""testing pad_across_processes"""				)
					test_pad_across_processes(snake_case				)
					state.print("""testing reduce_sum"""				)
					test_reduce_sum(snake_case				)
					state.print("""testing reduce_mean"""				)
					test_reduce_mean(snake_case				)


if __name__ == "__main__":
	main()






