
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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE_         =  {
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   Dict	)	-> Dict:






    """simple docstring"""




    for attribute in key.split('.'	):
        UpperCAmelCase       =   getattr(lowerCAmelCase	,			lowerCAmelCase	)

    if weight_type is not None:
        UpperCAmelCase       =   getattr(lowerCAmelCase	,			lowerCAmelCase	).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       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Union[str, Any]	)	-> Optional[int]:






    """simple docstring"""




    UpperCAmelCase       =   []
    UpperCAmelCase       =   fairseq_model.state_dict()

    UpperCAmelCase       =   hf_model.sew_d.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(
                lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			hf_model.config.feat_extract_norm == 'group'	,			)
            UpperCAmelCase       =   True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase       =   'sew_d.' + 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]:
                    UpperCAmelCase       =   True
                    if "*" in mapped_key:
                        UpperCAmelCase       =   name.split(lowerCAmelCase	)[0].split('.'	)[-2]
                        if not layer_index.isnumeric():
                            continue
                        UpperCAmelCase       =   mapped_key.replace('*'	,			lowerCAmelCase	)
                    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(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)
                continue
        if not is_used:
            unused_weights.append(lowerCAmelCase	)

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




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Dict	,			lowerCAmelCase			:   Optional[Any]	)	-> List[str]:






    """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(lowerCAmelCase	)




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   str	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   SEWDConfig()
    if is_finetuned:
        UpperCAmelCase       =   model.wav_encoder.wav_model.cfg
    else:
        UpperCAmelCase       =   model.cfg

    UpperCAmelCase       =   fs_config.conv_bias
    UpperCAmelCase       =   eval(fs_config.conv_feature_layers	)
    UpperCAmelCase       =   [x[0] for x in conv_layers]
    UpperCAmelCase       =   [x[1] for x in conv_layers]
    UpperCAmelCase       =   [x[2] for x in conv_layers]
    UpperCAmelCase       =   'gelu'
    UpperCAmelCase       =   'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
    UpperCAmelCase       =   0.0
    UpperCAmelCase       =   fs_config.activation_fn.name
    UpperCAmelCase       =   fs_config.encoder_embed_dim
    UpperCAmelCase       =   0.02
    UpperCAmelCase       =   fs_config.encoder_ffn_embed_dim
    UpperCAmelCase       =   1E-5
    UpperCAmelCase       =   fs_config.encoder_layerdrop
    UpperCAmelCase       =   fs_config.encoder_attention_heads
    UpperCAmelCase       =   fs_config.conv_pos_groups
    UpperCAmelCase       =   fs_config.conv_pos
    UpperCAmelCase       =   len(lowerCAmelCase	)
    UpperCAmelCase       =   fs_config.encoder_layers
    UpperCAmelCase       =   fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    UpperCAmelCase       =   fs_config.max_position_embeddings
    UpperCAmelCase       =   fs_config.position_buckets
    UpperCAmelCase       =   fs_config.share_att_key
    UpperCAmelCase       =   fs_config.relative_attention
    UpperCAmelCase       =   fs_config.position_biased_input
    UpperCAmelCase       =   tuple(fs_config.pos_att_type.split('|'	)	)
    UpperCAmelCase       =   fs_config.norm_rel_ebd

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

    UpperCAmelCase       =   'Wav2Vec2FeatureExtractor'
    UpperCAmelCase       =   'Wav2Vec2CTCTokenizer'

    return config




@torch.no_grad()
def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[str]=None	,			lowerCAmelCase			:   int=None	,			lowerCAmelCase			:   List[Any]=True	)	-> List[str]:






    """simple docstring"""




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

    if config_path is not None:
        UpperCAmelCase       =   SEWDConfig.from_pretrained(lowerCAmelCase	)
    else:
        UpperCAmelCase       =   convert_config(model[0]	,			lowerCAmelCase	)
    UpperCAmelCase       =   model[0].eval()

    UpperCAmelCase       =   True if config.feat_extract_norm == 'layer' else False
    UpperCAmelCase       =   WavaVecaFeatureExtractor(
        feature_size=1	,			sampling_rate=16_000	,			padding_value=0	,			do_normalize=lowerCAmelCase	,			return_attention_mask=lowerCAmelCase	,			)

    if is_finetuned:
        if dict_path:
            UpperCAmelCase       =   Dictionary.load(lowerCAmelCase	)

            # 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.pad_index
            UpperCAmelCase       =   target_dict.bos_index
            UpperCAmelCase       =   target_dict.eos_index
            UpperCAmelCase       =   len(target_dict.symbols	)
            UpperCAmelCase       =   os.path.join(lowerCAmelCase	,			'vocab.json'	)
            if not os.path.isdir(lowerCAmelCase	):
                logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase	)	)
                return
            os.makedirs(lowerCAmelCase	,			exist_ok=lowerCAmelCase	)
            with open(lowerCAmelCase	,			'w'	,			encoding='utf-8'	) as vocab_handle:
                json.dump(target_dict.indices	,			lowerCAmelCase	)
            UpperCAmelCase       =   WavaVecaCTCTokenizer(
                lowerCAmelCase	,			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=lowerCAmelCase	,			)
            UpperCAmelCase       =   WavaVecaProcessor(feature_extractor=lowerCAmelCase	,			tokenizer=lowerCAmelCase	)
            processor.save_pretrained(lowerCAmelCase	)

        UpperCAmelCase       =   SEWDForCTC(lowerCAmelCase	)
    else:
        UpperCAmelCase       =   SEWDModel(lowerCAmelCase	)
        feature_extractor.save_pretrained(lowerCAmelCase	)

    recursively_load_weights(lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    hf_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_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'''
   )
   SCREAMING_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
   )





"""simple docstring"""

import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()




def       lowercase__  (   lowerCAmelCase			:   List[Any]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Optional[Any]	)	-> Optional[Any]:






    """simple docstring"""




    UpperCAmelCase       =   FNetConfig.from_json_file(lowerCAmelCase	)
    print(F"Building PyTorch model from configuration: {config}"	)
    UpperCAmelCase       =   FNetForPreTraining(lowerCAmelCase	)

    UpperCAmelCase       =   restore_checkpoint(lowerCAmelCase	,			lowerCAmelCase	)
    UpperCAmelCase       =   checkpoint_dict['target']

    # Embeddings
    # Position IDs
    UpperCAmelCase       =   fnet_pretraining_model.state_dict()

    UpperCAmelCase       =   state_dict['fnet.embeddings.position_ids']
    UpperCAmelCase       =   {'fnet.embeddings.position_ids': position_ids}
    # Embedding Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['position']['embedding'][0]	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['type']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['hidden_mapping_in']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['layer_norm']['bias']	)

    # Encoder Layers
    for layer in range(config.num_hidden_layers	):
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['mixing_layer_norm']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['intermediate']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['kernel']	).T
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"feed_forward_{layer}"]['output']['bias']	)

        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['scale']	)
        UpperCAmelCase       =   torch.tensor(
            pretrained_model_params['encoder'][F"encoder_{layer}"]['output_layer_norm']['bias']	)

    # Pooler Layers
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['encoder']['pooler']['bias']	)

    # Masked LM Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['kernel']	).T
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_dense']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['scale']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_layer_norm']['bias']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['encoder']['embedder']['word']['embedding']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['predictions_output']['output_bias']	)
    UpperCAmelCase       =   torch.tensor(pretrained_model_params['predictions_output']['output_bias']	)

    # Seq Relationship Layers
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_kernel']	)
    UpperCAmelCase       =   torch.tensor(
        pretrained_model_params['classification']['output_bias']	)

    # Load State Dict
    fnet_pretraining_model.load_state_dict(lowerCAmelCase	)

    # Save PreTrained
    print(F"Saving pretrained model to {save_path}"	)
    fnet_pretraining_model.save_pretrained(lowerCAmelCase	)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_         =  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--fnet_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained FNet model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
   SCREAMING_SNAKE_CASE_         =  parser.parse_args()
   convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



