



'''simple docstring'''





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__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "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__      					=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        lowerCamelCase  (				_snake_case  :						Any     ,_snake_case  :						Optional[Any]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Dict     ,_snake_case  :						Tuple       ):





    '''simple docstring'''


    for attribute in key.split("."       ):
        lowercase__		       =						getattr(_snake_case     ,_snake_case       )

    if weight_type is not None:
        lowercase__		       =						getattr(_snake_case     ,_snake_case       ).shape
    else:
        lowercase__		       =						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":
        lowercase__		       =						value
    elif weight_type == "weight_g":
        lowercase__		       =						value
    elif weight_type == "weight_v":
        lowercase__		       =						value
    elif weight_type == "bias":
        lowercase__		       =						value
    elif weight_type == "running_mean":
        lowercase__		       =						value
    elif weight_type == "running_var":
        lowercase__		       =						value
    elif weight_type == "num_batches_tracked":
        lowercase__		       =						value
    elif weight_type == "inv_freq":
        lowercase__		       =						value
    else:
        lowercase__		       =						value

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




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						Any     ,_snake_case  :						Dict       ):





    '''simple docstring'''


    lowercase__		       =						[]
    lowercase__		       =						fairseq_model.state_dict()

    lowercase__		       =						hf_model.wavaveca_conformer.feature_extractor

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





    '''simple docstring'''


    lowercase__		       =						full_name.split("conv_layers."       )[-1]
    lowercase__		       =						name.split("."       )
    lowercase__		       =						int(items[0]       )
    lowercase__		       =						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.'''       )
            lowercase__		       =						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.'''       )
            lowercase__		       =						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.'''       )
            lowercase__		       =						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.'''       )
            lowercase__		       =						value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
    else:
        unused_weights.append(_snake_case       )




@torch.no_grad()
def        lowerCamelCase  (				_snake_case  :						Tuple     ,_snake_case  :						int     ,_snake_case  :						Tuple=None     ,_snake_case  :						List[str]=None     ,_snake_case  :						str=True       ):





    '''simple docstring'''


    if config_path is not None:
        lowercase__		       =						WavaVecaConformerConfig.from_pretrained(_snake_case     ,hidden_act="swish"       )
    else:
        lowercase__		       =						WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        lowercase__		       =						"rotary"

    if is_finetuned:
        if dict_path:
            lowercase__		       =						Dictionary.load(_snake_case       )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowercase__		       =						target_dict.pad_index
            lowercase__		       =						target_dict.bos_index
            lowercase__		       =						target_dict.eos_index
            lowercase__		       =						len(target_dict.symbols       )
            lowercase__		       =						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       )
            lowercase__		       =						target_dict.indices

            # fairseq has the <pad> and <s> switched
            lowercase__		       =						0
            lowercase__		       =						1
            with open(_snake_case     ,"w"     ,encoding="utf-8"       ) as vocab_handle:
                json.dump(_snake_case     ,_snake_case       )
            lowercase__		       =						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     ,)
            lowercase__		       =						True if config.feat_extract_norm == "layer" else False
            lowercase__		       =						WavaVecaFeatureExtractor(
                feature_size=1     ,sampling_rate=16_000     ,padding_value=0     ,do_normalize=_snake_case     ,return_attention_mask=_snake_case     ,)
            lowercase__		       =						WavaVecaProcessor(feature_extractor=_snake_case     ,tokenizer=_snake_case       )
            processor.save_pretrained(_snake_case       )

        lowercase__		       =						WavaVecaConformerForCTC(_snake_case       )
    else:
        lowercase__		       =						WavaVecaConformerForPreTraining(_snake_case       )

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

        lowercase__       ,       lowercase__       ,       lowercase__		       =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,task=_snake_case       )

    lowercase__		       =						model[0].eval()

    recursively_load_weights(_snake_case     ,_snake_case     ,not is_finetuned       )

    hf_wavavec.save_pretrained(_snake_case       )


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(
         "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
     )
     SCREAMING_SNAKE_CASE__      					=     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
     )









'''simple docstring'''





from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/blenderbot_small-90M": 512,
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Any						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :str						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :List[Any]						   =					BlenderbotSmallTokenizer



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							Optional[Any]:
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=UpperCAmelCase_     ,merges=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_     ,trim_offsets=UpperCAmelCase_     ,)     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,unk_token=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           lowercase__		       =						add_prefix_space



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							Optional[int]:
           lowercase__		       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[self.cls_token_id]

           if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




