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()
__lowercase									=   logging.get_logger(__name__)

__lowercase									=   {
    '''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    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    for attribute in key.split('''.'''      ):
        __UpperCamelCase	:Optional[int]         =						getattr(lowercase_    ,     lowercase_      )

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

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



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:Union[str, Any]         =						[]
    __UpperCamelCase	:Dict         =						fairseq_model.state_dict()

    __UpperCamelCase	:Optional[Any]         =						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase	:Union[str, Any]         =						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_    ,     lowercase_    ,     lowercase_    ,     lowercase_    ,     hf_model.config.feat_extract_norm == '''group'''    ,     )
            __UpperCamelCase	:List[str]         =						True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase	:Any         =						'''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]:
                    __UpperCamelCase	:Dict         =						True
                    if "*" in mapped_key:
                        __UpperCamelCase	:List[str]         =						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase	:Dict         =						mapped_key.replace('''*'''    ,     lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase	:str         =						'''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase	:Tuple         =						'''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase	:Dict         =						'''weight'''
                    elif "bias" in name:
                        __UpperCamelCase	:Any         =						'''bias'''
                    else:
                        __UpperCamelCase	:Any         =						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    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







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



def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:int         =						SEWConfig()
    if is_finetuned:
        __UpperCamelCase	:Dict         =						model.wav_encoder.wav_model.cfg
    else:
        __UpperCamelCase	:Tuple         =						model.cfg

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

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

    __UpperCamelCase	:Tuple         =						'''Wav2Vec2FeatureExtractor'''
    __UpperCamelCase	:Dict         =						'''Wav2Vec2CTCTokenizer'''

    return config



@torch.no_grad()
def lowerCamelCase    (  SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE    ,     SCREAMING_SNAKE_CASE=None    ,     SCREAMING_SNAKE_CASE=None    ,     SCREAMING_SNAKE_CASE=True      ):
    '''simple docstring'''







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

    if config_path is not None:
        __UpperCamelCase	:List[Any]         =						SEWConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase	:Any         =						convert_config(model[0]    ,     lowercase_      )
    __UpperCamelCase	:Tuple         =						model[0].eval()

    __UpperCamelCase	:Optional[int]         =						True if config.feat_extract_norm == '''layer''' else False
    __UpperCamelCase	:Optional[Any]         =						WavaVecaFeatureExtractor(
        feature_size=1    ,     sampling_rate=16_000    ,     padding_value=0    ,     do_normalize=lowercase_    ,     return_attention_mask=lowercase_    ,     )

    if is_finetuned:
        if dict_path:
            __UpperCamelCase	:List[Any]         =						Dictionary.load(lowercase_      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __UpperCamelCase	:List[Any]         =						target_dict.pad_index
            __UpperCamelCase	:Union[str, Any]         =						target_dict.bos_index
            __UpperCamelCase	:Tuple         =						target_dict.pad_index
            __UpperCamelCase	:List[Any]         =						target_dict.bos_index
            __UpperCamelCase	:Any         =						target_dict.eos_index
            __UpperCamelCase	:List[str]         =						len(target_dict.symbols      )
            __UpperCamelCase	:str         =						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	:Optional[Any]         =						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	:str         =						WavaVecaProcessor(feature_extractor=lowercase_    ,     tokenizer=lowercase_      )
            processor.save_pretrained(lowercase_      )

        __UpperCamelCase	:List[Any]         =						SEWForCTC(lowercase_      )
    else:
        __UpperCamelCase	:int         =						SEWModel(lowercase_      )
        feature_extractor.save_pretrained(lowercase_      )

    recursively_load_weights(lowercase_    ,     lowercase_    ,     lowercase_      )

    hf_model.save_pretrained(lowercase_      )


if __name__ == "__main__":
    __lowercase									=   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'''
    )
    __lowercase									=   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )









import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							List[Any]	    =	inspect.getfile(accelerate.test_utils)
							__UpperCAmelCase			:							Dict	    =	os.path.sep.join(mod_file.split(os.path.sep)[:-1] + ['''scripts''', '''test_script.py'''])
							__UpperCAmelCase			:							Union[str, Any]	    =	os.path.sep.join(inspect.getfile(self.__class__).split(os.path.sep)[:-1])


	@require_tpu
	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	F"\n            {self.test_dir}/xla_spawn.py\n            --num_cores 8\n            {self.test_file_path}\n        ".split()
							__UpperCAmelCase			:							Union[str, Any]	    =	[sys.executable] + distributed_args
							execute_subprocess_async(lowercase__				,     env=os.environ.copy())

