





"""simple docstring"""



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

a_					=     {
    """post_extract_proj""": """feature_projection.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.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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
a_					=     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def 						__lowercase	(  snake_case_    :    Optional[int]							,snake_case_    :    Optional[int]							,snake_case_    :    List[Any]							,snake_case_    :    List[Any]							,snake_case_    :    List[str]							,snake_case_    :    Any		)  ->Tuple:



    '''simple docstring'''







    for attribute in key.split('''.'''		):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                # those layers are only relevant for pretraining and should be dropped
                return

            if attribute == "ctc_proj":
                # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                __A       :		Union[str, Any]							=	'''lm_head'''

        __A       :		Tuple							=	getattr(snake_case_							,snake_case_		)

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

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







def 						__lowercase	(  snake_case_    :    Union[str, Any]							,snake_case_    :    List[str]							,snake_case_    :    List[str]		)  ->Optional[Any]:



    '''simple docstring'''







    __A       :		Union[str, Any]							=	[]
    __A       :		Optional[Any]							=	fairseq_model.state_dict()

    __A       :		int							=	hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        __A       :		Any							=	False
        if "conv_layers" in name:
            load_conv_layer(
                snake_case_							,snake_case_							,snake_case_							,snake_case_							,hf_model.config.feat_extract_norm == '''group'''							,)
            __A       :		List[Any]							=	True
        else:
            for key, mapped_key in MAPPING.items():
                __A       :		List[Any]							=	'''unispeech.''' + 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]:
                    __A       :		str							=	True
                    if "*" in mapped_key:
                        __A       :		Tuple							=	name.split(snake_case_		)[0].split('''.'''		)[-2]
                        __A       :		Tuple							=	mapped_key.replace('''*'''							,snake_case_		)
                    if "weight_g" in name:
                        __A       :		Dict							=	'''weight_g'''
                    elif "weight_v" in name:
                        __A       :		Dict							=	'''weight_v'''
                    elif "bias" in name:
                        __A       :		Tuple							=	'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __A       :		List[str]							=	'''weight'''
                    else:
                        __A       :		Optional[int]							=	None
                    set_recursively(snake_case_							,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 						__lowercase	(  snake_case_    :    str							,snake_case_    :    Optional[Any]							,snake_case_    :    int							,snake_case_    :    Tuple							,snake_case_    :    int		)  ->List[str]:



    '''simple docstring'''







    __A       :		Union[str, Any]							=	full_name.split('''conv_layers.'''		)[-1]
    __A       :		List[str]							=	name.split('''.'''		)
    __A       :		List[Any]							=	int(items[0]		)
    __A       :		int							=	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       :		Union[str, Any]							=	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       :		Dict							=	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       :		int							=	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       :		Optional[int]							=	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 						__lowercase	(  snake_case_    :    List[Any]							,snake_case_    :    Any							,snake_case_    :    Optional[int]=None							,snake_case_    :    List[str]=None							,snake_case_    :    List[Any]=True		)  ->int:



    '''simple docstring'''







    if config_path is not None:
        __A       :		int							=	UniSpeechConfig.from_pretrained(snake_case_		)
    else:
        __A       :		str							=	UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            __A       :		Optional[int]							=	Dictionary.load_from_json(snake_case_		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __A       :		Dict							=	target_dict.pad_index
            __A       :		Any							=	target_dict.bos_index
            __A       :		List[str]							=	target_dict.eos_index
            __A       :		str							=	len(target_dict.symbols		)
            __A       :		List[Any]							=	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_		)
            __A       :		Optional[int]							=	target_dict.indices

            # fairseq has the <pad> and <s> switched
            __A       :		Union[str, Any]							=	42
            __A       :		str							=	43
            with open(snake_case_							,'''w'''							,encoding='''utf-8'''		) as vocab_handle:
                json.dump(snake_case_							,snake_case_		)
            __A       :		Optional[Any]							=	WavaVecaPhonemeCTCTokenizer(
                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_							,)
            __A       :		Tuple							=	True if config.feat_extract_norm == '''layer''' else False
            __A       :		Tuple							=	WavaVecaFeatureExtractor(
                feature_size=1							,sampling_rate=16000							,padding_value=0							,do_normalize=snake_case_							,return_attention_mask=snake_case_							,)
            __A       :		int							=	WavaVecaProcessor(feature_extractor=snake_case_							,tokenizer=snake_case_		)
            processor.save_pretrained(snake_case_		)

        __A       :		List[Any]							=	UniSpeechForCTC(snake_case_		)
    else:
        __A       :		Optional[int]							=	UniSpeechForPreTraining(snake_case_		)

    if is_finetuned:
        __A					,			__A					,			__A       :		List[Any]							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		), '''w2v_path''': checkpoint_path}		)
    else:
        __A					,			__A					,			__A       :		Dict							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

    __A       :		List[str]							=	model[0].eval()

    recursively_load_weights(snake_case_							,snake_case_							,snake_case_		)

    hf_unispeech.save_pretrained(snake_case_		)


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(
           """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
       )
       a_					=     parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )











"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_					=     logging.get_logger(__name__)

a_					=     {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""poolformer"""

 def __init__(       self   ,		__lowerCamelCase=3   ,		__lowerCamelCase=16   ,		__lowerCamelCase=16   ,		__lowerCamelCase=3   ,		__lowerCamelCase=4.0   ,		__lowerCamelCase=[2, 2, 6, 2]   ,		__lowerCamelCase=[64, 128, 320, 512]   ,		__lowerCamelCase=[7, 3, 3, 3]   ,		__lowerCamelCase=[4, 2, 2, 2]   ,		__lowerCamelCase=[2, 1, 1, 1]   ,		__lowerCamelCase=4   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase=0.0_2   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		str							=	num_channels
     __A       :		List[str]							=	patch_size
     __A       :		str							=	stride
     __A       :		Any							=	padding
     __A       :		Any							=	pool_size
     __A       :		Dict							=	hidden_sizes
     __A       :		Optional[Any]							=	mlp_ratio
     __A       :		Any							=	depths
     __A       :		List[str]							=	patch_sizes
     __A       :		Union[str, Any]							=	strides
     __A       :		List[str]							=	num_encoder_blocks
     __A       :		Optional[int]							=	drop_path_rate
     __A       :		Union[str, Any]							=	hidden_act
     __A       :		Optional[Any]							=	use_layer_scale
     __A       :		List[Any]							=	layer_scale_init_value
     __A       :		List[Any]							=	initializer_range
     super().__init__(**__lowerCamelCase    )






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	version.parse("""1.11"""	)

 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ]    )







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return 2e-3




