



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_   :Optional[int]        =      logging.get_logger(__name__)

a_   :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",
    "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_   :Any        =      [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def        lowercase_							(A : Dict		,					A : Tuple		,					A : List[str]		,					A : str		,					A : List[str]		,					A : int      ):
       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
                            snake_case__							:	Optional[Any]   	=	'lm_head'

              snake_case__							:	int   	=	getattr(A		,					A      )

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

       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''      )
def        lowercase_							(A : List[Any]		,					A : List[Any]		,					A : List[str]      ):
       snake_case__							:	Optional[int]   	=	[]
       snake_case__							:	Optional[Any]   	=	fairseq_model.state_dict()

       snake_case__							:	Tuple   	=	hf_model.unispeech.feature_extractor

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

       logger.warning(F'''Unused weights: {unused_weights}'''      )
def        lowercase_							(A : Union[str, Any]		,					A : List[Any]		,					A : Dict		,					A : Any		,					A : Optional[Any]      ):
       snake_case__							:	Optional[int]   	=	full_name.split('conv_layers.'      )[-1]
       snake_case__							:	str   	=	name.split('.'      )
       snake_case__							:	Union[str, Any]   	=	int(items[0]      )
       snake_case__							:	Optional[Any]   	=	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.'''
                     )
                     snake_case__							:	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.'''
                     )
                     snake_case__							:	List[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:
                     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."
                     )
                     snake_case__							:	List[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.'''
                     )
                     snake_case__							:	str   	=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
       else:
              unused_weights.append(A      )
@torch.no_grad()
def        lowercase_							(A : Optional[Any]		,					A : Union[str, Any]		,					A : List[Any]=None		,					A : int=None		,					A : List[Any]=True      ):
       if config_path is not None:
              snake_case__							:	Tuple   	=	UniSpeechConfig.from_pretrained(A      )
       else:
              snake_case__							:	Tuple   	=	UniSpeechConfig()

       if is_finetuned:
              if dict_path:
                     snake_case__							:	Tuple   	=	Dictionary.load_from_json(A      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     snake_case__							:	Any   	=	target_dict.pad_index
                     snake_case__							:	List[str]   	=	target_dict.bos_index
                     snake_case__							:	List[Any]   	=	target_dict.eos_index
                     snake_case__							:	Optional[Any]   	=	len(target_dict.symbols      )
                     snake_case__							:	List[Any]   	=	os.path.join(A		,					'vocab.json'      )
                     if not os.path.isdir(A      ):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A      )      )
                            return
                     os.makedirs(A		,					exist_ok=A      )
                     snake_case__							:	List[Any]   	=	target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     snake_case__							:	Optional[Any]   	=	4_2
                     snake_case__							:	Tuple   	=	4_3
                     with open(A		,					'w'		,					encoding='utf-8'      ) as vocab_handle:
                            json.dump(A		,					A      )
                     snake_case__							:	Tuple   	=	WavaVecaPhonemeCTCTokenizer(
                         A		,					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=A		,					)
                     snake_case__							:	int   	=	True if config.feat_extract_norm == 'layer' else False
                     snake_case__							:	Union[str, Any]   	=	WavaVecaFeatureExtractor(
                         feature_size=1		,					sampling_rate=1_6_0_0_0		,					padding_value=0		,					do_normalize=A		,					return_attention_mask=A		,					)
                     snake_case__							:	Union[str, Any]   	=	WavaVecaProcessor(feature_extractor=A		,					tokenizer=A      )
                     processor.save_pretrained(A      )

              snake_case__							:	int   	=	UniSpeechForCTC(A      )
       else:
              snake_case__							:	Optional[int]   	=	UniSpeechForPreTraining(A      )

       if is_finetuned:
              snake_case__	,				snake_case__	,				snake_case__							:	str   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]		,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      ), 'w2v_path': checkpoint_path}      )
       else:
              snake_case__	,				snake_case__	,				snake_case__							:	Tuple   	=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

       snake_case__							:	Tuple   	=	model[0].eval()

       recursively_load_weights(A		,					A		,					A      )

       hf_unispeech.save_pretrained(A      )


if __name__ == "__main__":
 a_   :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"
 )
 a_   :List[Any]        =      parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )











from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


a_   :List[Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  str					)   ->Dict:
            super().__init__(**_snake_case					)

            if self.framework != "pt":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)
            # No specific FOR_XXX available yet






     def __call__(       self  :  Union[str, Any], _snake_case  :  Union[np.ndarray, bytes, str], **_snake_case  :  Tuple					)   ->Dict:
            return super().__call__(_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, **_snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	{}
            if "candidate_labels" in kwargs:
                   snake_case__							:	str   	=	kwargs['candidate_labels']
            if "hypothesis_template" in kwargs:
                   snake_case__							:	str   	=	kwargs['hypothesis_template']

            return preprocess_params, {}, {}






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[int]=None, _snake_case  :  List[str]="This is a sound of {}."					)   ->int:
            if isinstance(_snake_case, _snake_case					):
                   if audio.startswith('http://'					) or audio.startswith('https://'					):
                          # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                          # like http_huggingface_co.png
                          snake_case__							:	List[Any]   	=	requests.get(_snake_case					).content
                   else:
                          with open(_snake_case, 'rb'					) as f:
                                 snake_case__							:	Union[str, Any]   	=	f.read()

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	List[Any]   	=	ffmpeg_read(_snake_case, self.feature_extractor.sampling_rate					)

            if not isinstance(_snake_case, np.ndarray					):
                   raise ValueError('We expect a numpy ndarray as input'					)
            if len(audio.shape					) != 1:
                   raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'					)

            snake_case__							:	Tuple   	=	self.feature_extractor(
                [audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='pt'					)
            snake_case__							:	int   	=	candidate_labels
            snake_case__							:	int   	=	[hypothesis_template.format(_snake_case					) for x in candidate_labels]
            snake_case__							:	Optional[int]   	=	self.tokenizer(_snake_case, return_tensors=self.framework, padding=_snake_case					)
            snake_case__							:	List[Any]   	=	[text_inputs]
            return inputs






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any]					)   ->int:
            snake_case__							:	Optional[int]   	=	model_inputs.pop('candidate_labels'					)
            snake_case__							:	str   	=	model_inputs.pop('text_inputs'					)
            if isinstance(text_inputs[0], _snake_case					):
                   snake_case__							:	Optional[Any]   	=	text_inputs[0]
            else:
                   # Batching case.
                   snake_case__							:	int   	=	text_inputs[0][0]

            snake_case__							:	Any   	=	self.model(**_snake_case, **_snake_case					)

            snake_case__							:	List[Any]   	=	{
                'candidate_labels': candidate_labels,
                'logits': outputs.logits_per_audio,
            }
            return model_outputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str					)   ->List[str]:
            snake_case__							:	int   	=	model_outputs.pop('candidate_labels'					)
            snake_case__							:	List[Any]   	=	model_outputs['logits'][0]

            if self.framework == "pt":
                   snake_case__							:	Tuple   	=	logits.softmax(dim=0					)
                   snake_case__							:	Union[str, Any]   	=	probs.tolist()
            else:
                   raise ValueError('`tf` framework not supported.'					)

            snake_case__							:	Union[str, Any]   	=	[
                {'score': score, 'label': candidate_label}
                for score, candidate_label in sorted(zip(_snake_case, _snake_case					), key=lambda _snake_case					: -x[0]					)
            ]
            return result






