
'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase			:				str    		=			logging.get_logger(__name__)

lowercase			:				Optional[Any]    		=			{
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







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




    '''simple docstring'''



    for attribute in key.split('''.'''						):
        A       :   Dict					=						getattr(snake_case__		,				snake_case__						)

    if weight_type is not None:
        A       :   Dict					=						getattr(snake_case__		,				snake_case__						).shape
    else:
        A       :   str					=						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       :   List[str]					=						value
    elif weight_type == "weight_g":
        A       :   str					=						value
    elif weight_type == "weight_v":
        A       :   Optional[Any]					=						value
    elif weight_type == "bias":
        A       :   List[str]					=						value
    else:
        A       :   Tuple					=						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__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						[]
    A       :   Tuple					=						fairseq_model.state_dict()

    A       :   Union[str, Any]					=						hf_model.hubert.feature_extractor if is_finetuned else hf_model.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       :   Optional[Any]					=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :   List[str]					=						'''hubert.''' + 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] and not is_finetuned):
                    A       :   str					=						True
                    if "*" in mapped_key:
                        A       :   str					=						name.split(snake_case__						)[0].split('''.'''						)[-2]
                        A       :   int					=						mapped_key.replace('''*'''		,				snake_case__						)
                    if "weight_g" in name:
                        A       :   List[str]					=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :   str					=						'''weight_v'''
                    elif "weight" in name:
                        A       :   List[str]					=						'''weight'''
                    elif "bias" in name:
                        A       :   Optional[Any]					=						'''bias'''
                    else:
                        A       :   Optional[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       :   Optional[int]					=						full_name.split('''conv_layers.'''						)[-1]
    A       :   Dict					=						name.split('''.'''						)
    A       :   Union[str, Any]					=						int(items[0]						)
    A       :   str					=						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       :   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.'
            )
            A       :   Any					=						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       :   Union[str, 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.'
            )
            A       :   Tuple					=						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       :   Tuple					=						HubertConfig.from_pretrained(snake_case__						)
    else:
        A       :   Dict					=						HubertConfig()

    if is_finetuned:
        if dict_path:
            A       :   Any					=						Dictionary.load(snake_case__						)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A       :   str					=						target_dict.pad_index
            A       :   Optional[int]					=						target_dict.bos_index
            A       :   Optional[int]					=						target_dict.eos_index
            A       :   Optional[int]					=						len(target_dict.symbols						)
            A       :   Union[str, 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__						)
            with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as vocab_handle:
                json.dump(target_dict.indices		,				snake_case__						)
            A       :   Optional[int]					=						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__		,				)
            A       :   Optional[Any]					=						True if config.feat_extract_norm == '''layer''' else False
            A       :   List[str]					=						WavaVecaFeatureExtractor(
                feature_size=1		,				sampling_rate=1_6000		,				padding_value=0		,				do_normalize=snake_case__		,				return_attention_mask=snake_case__		,				)
            A       :   Dict					=						WavaVecaProcessor(feature_extractor=snake_case__		,				tokenizer=snake_case__						)
            processor.save_pretrained(snake_case__						)

        A       :   Union[str, Any]					=						HubertForCTC(snake_case__						)
    else:
        A       :   List[str]					=						HubertModel(snake_case__						)

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

    A       :   int					=						model[0].eval()

    recursively_load_weights(snake_case__		,				snake_case__		,				snake_case__						)

    hf_wavavec.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Optional[Any]    		=			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			:				Dict    		=			parser.parse_args()
      convert_hubert_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )






'''simple docstring'''




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

lowercase			:				str    		=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case						)
class  A				(							__snake_case						):





     def __init__(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         self.check_model_type(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A,  A       :   Dict					=						{}, {}
         if padding is not None:
             A       :   List[str]					=						padding
         if truncation is not None:
             A       :   Dict					=						truncation
         if top_k is not None:
             A       :   Optional[Any]					=						top_k
         return preprocess_params, {}, postprocess_params





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			(Image.Image, str)	) and isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						{'''image''': image, '''question''': question}
         else:
             A       :   Any					=						image
         A       :   Any					=						super().__call__(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         return results





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_image(inputs['''image''']	)
         A       :   Optional[Any]					=						self.tokenizer(
             inputs['''question''']						,			return_tensors=self.framework						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						self.image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors=self.framework	)
         model_inputs.update(SCREAMING_SNAKE_CASE	)
         return model_inputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[Any]					=						self.model(**SCREAMING_SNAKE_CASE	)
         return model_outputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=5	) ->				int:






         """simple docstring"""






         if top_k > self.model.config.num_labels:
             A       :   Dict					=						self.model.config.num_labels

         if self.framework == "pt":
             A       :   Optional[int]					=						model_outputs.logits.sigmoid()[0]
             A,  A       :   int					=						probs.topk(SCREAMING_SNAKE_CASE	)
         else:
             raise ValueError(F'Unsupported framework: {self.framework}'	)

         A       :   int					=						scores.tolist()
         A       :   List[str]					=						ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)]




