

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()
_lowerCamelCase      :					List[str]					    =      logging.get_logger(__name__)

_lowerCamelCase      :					Any					    =      {
    '''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      A__					(			__A  :						Union[str, Any]     ,				__A  :						Optional[Any]     ,				__A  :						Dict     ,				__A  :						List[str]     ,				__A  :						Dict			)					->List[str]:
      for attribute in key.split('''.'''			):
            __A    =getattr(__A     ,				__A			)

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

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





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      __A    =[]
      __A    =fairseq_model.state_dict()

      __A    =hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            __A    =False
            if "conv_layers" in name:
                  load_conv_layer(
                      __A     ,				__A     ,				__A     ,				__A     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
                  __A    =True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A    ='''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    =True
                              if "*" in mapped_key:
                                    __A    =name.split(__A			)[0].split('''.'''			)[-2]
                                    __A    =mapped_key.replace('''*'''     ,				__A			)
                              if "weight_g" in name:
                                    __A    ='''weight_g'''
                              elif "weight_v" in name:
                                    __A    ='''weight_v'''
                              elif "weight" in name:
                                    __A    ='''weight'''
                              elif "bias" in name:
                                    __A    ='''bias'''
                              else:
                                    __A    =None
                              set_recursively(__A     ,				__A     ,				__A     ,				__A     ,				__A			)
                        continue
            if not is_used:
                  unused_weights.append(__A			)

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





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





def      A__					(			__A  :						Tuple     ,				__A  :						Tuple			)					->str:
      __A    =SEWConfig()
      if is_finetuned:
            __A    =model.wav_encoder.wav_model.cfg
      else:
            __A    =model.cfg

      __A    =fs_config.conv_bias
      __A    =eval(fs_config.conv_feature_layers			)
      __A    =[x[0] for x in conv_layers]
      __A    =[x[1] for x in conv_layers]
      __A    =[x[2] for x in conv_layers]
      __A    ='''gelu'''
      __A    ='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      __A    =0.0
      __A    =fs_config.activation_fn.name
      __A    =fs_config.encoder_embed_dim
      __A    =0.02
      __A    =fs_config.encoder_ffn_embed_dim
      __A    =1e-5
      __A    =fs_config.encoder_layerdrop
      __A    =fs_config.encoder_attention_heads
      __A    =fs_config.conv_pos_groups
      __A    =fs_config.conv_pos
      __A    =len(__A			)
      __A    =fs_config.encoder_layers
      __A    =fs_config.squeeze_factor

      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
            __A    =model.cfg
            __A    =fs_config.final_dropout
            __A    =fs_config.layerdrop
      __A    =fs_config.activation_dropout
      __A    =fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      __A    =fs_config.attention_dropout
      __A    =fs_config.dropout_input
      __A    =fs_config.dropout
      __A    =fs_config.mask_channel_length
      __A    =fs_config.mask_channel_prob
      __A    =fs_config.mask_length
      __A    =fs_config.mask_prob

      __A    ='''Wav2Vec2FeatureExtractor'''
      __A    ='''Wav2Vec2CTCTokenizer'''

      return config





@torch.no_grad()
def      A__					(			__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Tuple=None     ,				__A  :						Tuple=None     ,				__A  :						List[Any]=True			)					->Any:

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

      if config_path is not None:
            __A    =SEWConfig.from_pretrained(__A			)
      else:
            __A    =convert_config(model[0]     ,				__A			)
      __A    =model[0].eval()

      __A    =True if config.feat_extract_norm == '''layer''' else False
      __A    =WavaVecaFeatureExtractor(
          feature_size=1     ,				sampling_rate=1_60_00     ,				padding_value=0     ,				do_normalize=__A     ,				return_attention_mask=__A     ,				)

      if is_finetuned:
            if dict_path:
                  __A    =Dictionary.load(__A			)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __A    =target_dict.pad_index
                  __A    =target_dict.bos_index
                  __A    =target_dict.pad_index
                  __A    =target_dict.bos_index
                  __A    =target_dict.eos_index
                  __A    =len(target_dict.symbols			)
                  __A    =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			)
                  with open(__A     ,				'''w'''     ,				encoding='''utf-8'''			) as vocab_handle:
                        json.dump(target_dict.indices     ,				__A			)
                  __A    =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     ,				)
                  __A    =WavaVecaProcessor(feature_extractor=__A     ,				tokenizer=__A			)
                  processor.save_pretrained(__A			)

            __A    =SEWForCTC(__A			)
      else:
            __A    =SEWModel(__A			)
            feature_extractor.save_pretrained(__A			)

      recursively_load_weights(__A     ,				__A     ,				__A			)

      hf_model.save_pretrained(__A			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      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'''
   )
   _lowerCamelCase      :					Dict					    =      parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
else:
   from .modeling_text_unet import UNetFlatConditionModel
   from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
   from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
   from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
   from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline


