




# Copyright 2021 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 argparse
import os

from accelerate.test_utils import execute_subprocess_async




def 					__SCREAMING_SNAKE_CASE   (  lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    if subparsers is not None:
        __UpperCamelCase  		=						subparsers.add_parser('''test'''      )
    else:
        __UpperCamelCase  		=						argparse.ArgumentParser('''Accelerate test command'''      )

    parser.add_argument(
        '''--config_file'''					,     default=A__					,     help=(
            '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
            '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
            '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
            '''with \'huggingface\'.'''
        )					,     )

    if subparsers is not None:
        parser.set_defaults(func=A__      )
    return parser




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py''']      )

    if args.config_file is None:
        __UpperCamelCase  		=						script_name
    else:
        __UpperCamelCase  		=						F"--config_file={args.config_file} {script_name}"

    __UpperCamelCase  		=						['''accelerate-launch'''] + test_args.split()
    __UpperCamelCase  		=						execute_subprocess_async(A__					,     env=os.environ.copy()      )
    if result.returncode == 0:
        print('''Test is a success! You are ready for your distributed training!'''      )




def 					__SCREAMING_SNAKE_CASE   (  )       ->	Optional[int]:

    """simple docstring"""


    __UpperCamelCase  		=						test_command_parser()
    __UpperCamelCase  		=						parser.parse_args()
    test_command(A__      )


if __name__ == "__main__":
 main()








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()
a_   		=     logging.get_logger(__name__)

a_   		=     {
    "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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      ).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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[str]:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.sew.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(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase  		=						'''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  		=						True
                    if "*" in mapped_key:
                        __UpperCamelCase  		=						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    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(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
                continue
        if not is_used:
            unused_weights.append(lowercase_      )

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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """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(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						SEWConfig()
    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(lowercase_      )
    __UpperCamelCase  		=						fs_config.encoder_layers
    __UpperCamelCase  		=						fs_config.squeeze_factor

    # 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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_=True      )       ->	Any:

    """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  		=						SEWConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						convert_config(model[0]					,     lowercase_      )
    __UpperCamelCase  		=						model[0].eval()

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

    if is_finetuned:
        if dict_path:
            __UpperCamelCase  		=						Dictionary.load(lowercase_      )

            # 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(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  		=						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  		=						WavaVecaProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )
            processor.save_pretrained(lowercase_      )

        __UpperCamelCase  		=						SEWForCTC(lowercase_      )
    else:
        __UpperCamelCase  		=						SEWModel(lowercase_      )
        feature_extractor.save_pretrained(lowercase_      )

    recursively_load_weights(lowercase_					,     lowercase_					,     lowercase_      )

    hf_model.save_pretrained(lowercase_      )


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






