'''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()
SCREAMING_SNAKE_CASE_:	str    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	Union[str, 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_			:					int   ,       snake_case_			:					Optional[int]   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					Dict   ,       snake_case_			:					Optional[Any]     )       -> Union[str, Any]:


     '''simple docstring'''




     for attribute in key.split("."     ):
          UpperCAmelCase_			       =  getattr(snake_case_   ,       snake_case_     )

     if weight_type is not None:
          UpperCAmelCase_			       =  getattr(snake_case_   ,       snake_case_     ).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       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					List[str]   ,       snake_case_			:					int     )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  []
     UpperCAmelCase_			       =  fairseq_model.state_dict()

     UpperCAmelCase_			       =  hf_model.hubert.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(
                   snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       hf_model.config.feat_extract_norm == "group"   ,       )
               UpperCAmelCase_			       =  True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_			       =  "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):
                         UpperCAmelCase_			       =  True
                         if "*" in mapped_key:
                              UpperCAmelCase_			       =  name.split(snake_case_     )[0].split("."     )[-2]
                              UpperCAmelCase_			       =  mapped_key.replace("*"   ,       snake_case_     )
                         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(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_			:					Optional[int]   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					Any   ,       snake_case_			:					int   ,       snake_case_			:					Optional[int]     )       -> List[Any]:


     '''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(snake_case_     )





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Union[str, Any]   ,       snake_case_			:					str=None   ,       snake_case_			:					Tuple=None   ,       snake_case_			:					Dict=True     )       -> Optional[int]:


     '''simple docstring'''




     if config_path is not None:
          UpperCAmelCase_			       =  HubertConfig.from_pretrained(snake_case_     )
     else:
          UpperCAmelCase_			       =  HubertConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_			       =  Dictionary.load(snake_case_     )

               # 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.eos_index
               UpperCAmelCase_			       =  len(target_dict.symbols     )
               UpperCAmelCase_			       =  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_     )
               UpperCAmelCase_			       =  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_   ,       )
               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=snake_case_   ,       return_attention_mask=snake_case_   ,       )
               UpperCAmelCase_			       =  WavaVecaProcessor(feature_extractor=snake_case_   ,       tokenizer=snake_case_     )
               processor.save_pretrained(snake_case_     )

          UpperCAmelCase_			       =  HubertForCTC(snake_case_     )
     else:
          UpperCAmelCase_			       =  HubertModel(snake_case_     )

     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]     )

     UpperCAmelCase_			       =  model[0].eval()

     recursively_load_weights(snake_case_   ,       snake_case_   ,       snake_case_     )

     hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	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'
    )
    SCREAMING_SNAKE_CASE_:	Optional[Any]    =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 collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)





@add_end_docstrings(__lowerCAmelCase)
class      lowerCAmelCase (			__lowerCAmelCase):


       def __init__( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Optional[int]:



            '''simple docstring'''




            super().__init__(**__SCREAMING_SNAKE_CASE	)

            requires_backends(self     ,	'''vision'''	)
            self.check_model_type(
                TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
                if self.framework == '''tf'''
                else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING	)


       def __call__( self     ,	__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	List[Any]:



            '''simple docstring'''




            return super().__call__(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	{}
            if "candidate_labels" in kwargs:
                 __snake_case            =	kwargs['''candidate_labels''']
            if "hypothesis_template" in kwargs:
                 __snake_case            =	kwargs['''hypothesis_template''']

            return preprocess_params, {}, {}


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="This is a photo of {}."	)   ->	Optional[Any]:



            '''simple docstring'''




            __snake_case            =	load_image(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	self.image_processor(images=[image]     ,	return_tensors=self.framework	)
            __snake_case            =	candidate_labels
            __snake_case            =	[hypothesis_template.format(__SCREAMING_SNAKE_CASE	) for x in candidate_labels]
            __snake_case            =	self.tokenizer(__SCREAMING_SNAKE_CASE     ,	return_tensors=self.framework     ,	padding=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	[text_inputs]
            return inputs


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	model_inputs.pop('''candidate_labels'''	)
            __snake_case            =	model_inputs.pop('''text_inputs'''	)
            if isinstance(text_inputs[0]     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	text_inputs[0]
            else:
                 # Batching case.
                 __snake_case            =	text_inputs[0][0]

            __snake_case            =	self.model(**__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	{
                '''candidate_labels''': candidate_labels,
                '''logits''': outputs.logits_per_image,
            }
            return model_outputs




       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	model_outputs.pop('''candidate_labels'''	)
            __snake_case            =	model_outputs['''logits'''][0]
            if self.framework == "pt":
                 __snake_case            =	logits.softmax(dim=-1	).squeeze(-1	)
                 __snake_case            =	probs.tolist()
                 if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                      __snake_case            =	[scores]
            elif self.framework == "tf":
                 __snake_case            =	stable_softmax(__SCREAMING_SNAKE_CASE     ,	axis=-1	)
                 __snake_case            =	probs.numpy().tolist()
            else:
                 raise ValueError(F'''Unsupported framework: {self.framework}'''	)

            __snake_case            =	[
                {'''score''': score, '''label''': candidate_label}
                for score, candidate_label in sorted(zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)     ,	key=lambda __SCREAMING_SNAKE_CASE	: -x[0]	)
            ]
            return result






