
'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowercase			:				List[str]    		=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    for attribute in key.split('''.'''						):
        A       :   List[str]					=						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

    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":
        A       :   List[str]					=						value
    elif weight_type == "weight_g":
        A       :   Tuple					=						value
    elif weight_type == "weight_v":
        A       :   int					=						value
    elif weight_type == "bias":
        A       :   int					=						value
    else:
        A       :   List[str]					=						value

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						[]
    A       :   int					=						fairseq_model.state_dict()

    A       :   List[str]					=						hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        A       :   Optional[int]					=						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       :   Union[str, Any]					=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :   Optional[Any]					=						'''unispeech_sat.''' + 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]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''						)[:-1]						) != key):
                        # special case since naming is very similar
                        continue
                    A       :   List[Any]					=						True
                    if "*" in mapped_key:
                        A       :   List[Any]					=						name.split(snake_case__						)[0].split('''.'''						)[-2]
                        A       :   Any					=						mapped_key.replace('''*'''		,				snake_case__						)
                    if "weight_g" in name:
                        A       :   str					=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :   Dict					=						'''weight_v'''
                    elif "bias" in name:
                        A       :   Union[str, Any]					=						'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        A       :   Optional[Any]					=						'''weight'''
                    else:
                        A       :   Any					=						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__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   int					=						full_name.split('''conv_layers.'''						)[-1]
    A       :   Any					=						name.split('''.'''						)
    A       :   Dict					=						int(items[0]						)
    A       :   str					=						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.'						)
            A       :   Any					=						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.'						)
            A       :   str					=						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[layer_id].layer_norm.bias.data.shape} was found.'						)
            A       :   List[Any]					=						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[layer_id].layer_norm.weight.data.shape} was found.'						)
            A       :   List[str]					=						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__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=True						):




    '''simple docstring'''



    if config_path is not None:
        A       :   List[str]					=						UniSpeechSatConfig.from_pretrained(snake_case__						)
    else:
        A       :   Optional[int]					=						UniSpeechSatConfig()

    A       :   int					=						''''''

    if is_finetuned:
        A       :   List[str]					=						UniSpeechSatForCTC(snake_case__						)
    else:
        A       :   int					=						UniSpeechSatForPreTraining(snake_case__						)

    A,  A,  A       :   str					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]		,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
    A       :   List[str]					=						model[0].eval()

    recursively_load_weights(snake_case__		,				snake_case__						)

    hf_wavavec.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			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'
      )
      lowercase			:				str    		=			parser.parse_args()
      convert_unispeech_sat_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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''bert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						vocab_size
         A       :   Optional[Any]					=						hidden_size
         A       :   List[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Dict					=						hidden_act
         A       :   Optional[Any]					=						intermediate_size
         A       :   List[Any]					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   List[str]					=						type_vocab_size
         A       :   Dict					=						initializer_range
         A       :   str					=						layer_norm_eps
         A       :   int					=						position_embedding_type
         A       :   Dict					=						use_cache
         A       :   str					=						classifier_dropout





class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         if self.task == "multiple-choice":
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A       :   Optional[int]					=						{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]	)




