import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowerCAmelCase__     						=			{
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


lowerCAmelCase__     						=			{
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowerCAmelCase__     						=			{
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION






lowerCAmelCase__     						=			collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowerCAmelCase__     						=			collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowerCAmelCase__     						=			r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '
@add_start_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			:






 '''simple docstring'''






 def __call__(      self :  Optional[int]      ,   lowercase__ :  List[str]      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Optional[str] = None      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Union[bool, str] = False      ,   lowercase__ :  Optional[int] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  Optional[bool] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



        if titles is None and texts is None:
               return super().__call__(
                   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        elif titles is None or texts is None:
               lowerCAmelCase__					     = titles if texts is None else texts
               return super().__call__(
                   lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__      ,   return_attention_mask=lowercase__      ,   **lowercase__      ,   )
        lowerCAmelCase__					     = titles if not isinstance(lowercase__      ,   lowercase__) else [titles]
        lowerCAmelCase__					     = texts if not isinstance(lowercase__      ,   lowercase__) else [texts]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = questions if not isinstance(lowercase__      ,   lowercase__) else [questions] * n_passages
        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   F"""There should be as many titles than texts but got {len(lowercase__)} titles and {len(lowercase__)} texts.""")
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = super().__call__(lowercase__      ,   add_special_tokens=lowercase__      ,   padding=lowercase__      ,   truncation=lowercase__)['input_ids']
        lowerCAmelCase__					     = {
            'input_ids': [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(lowercase__      ,   lowercase__)
            ]
        }
        if return_attention_mask is not False:
               lowerCAmelCase__					     = []
               for input_ids in encoded_inputs["input_ids"]:
                      attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               lowerCAmelCase__					     = attention_mask
        return self.pad(lowercase__      ,   padding=lowercase__      ,   max_length=lowercase__      ,   return_tensors=lowercase__)




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  BatchEncoding      ,   lowercase__ :  DPRReaderOutput      ,   lowercase__ :  int = 16      ,   lowercase__ :  int = 64      ,   lowercase__ :  int = 4      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = reader_input['input_ids']
        lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = reader_output[:3]
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = sorted(range(lowercase__)      ,   reverse=lowercase__      ,   key=relevance_logits.__getitem__)
        lowerCAmelCase__					     = []
        for doc_id in sorted_docs:
               lowerCAmelCase__					     = list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               lowerCAmelCase__					     = sequence_ids.index(self.sep_token_id      ,   2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                      lowerCAmelCase__					     = sequence_ids.index(self.pad_token_id)
               else:
                      lowerCAmelCase__					     = len(lowercase__)

               lowerCAmelCase__					     = self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len]      ,   end_logits=end_logits[doc_id][passage_offset:sequence_len]      ,   max_answer_length=lowercase__      ,   top_spans=lowercase__      ,   )
               for start_index, end_index in best_spans:
                      start_index += passage_offset
                      end_index += passage_offset
                      nbest_spans_predictions.append(
                          DPRSpanPrediction(
                              span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]      ,   relevance_score=relevance_logits[doc_id]      ,   doc_id=lowercase__      ,   start_index=lowercase__      ,   end_index=lowercase__      ,   text=self.decode(sequence_ids[start_index : end_index + 1])      ,   ))
               if len(lowercase__) >= num_spans:
                      break
        return nbest_spans_predictions[:num_spans]




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  List[int]      ,   lowercase__ :  int      ,   lowercase__ :  int      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = []
        for start_index, start_score in enumerate(lowercase__):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                      scores.append(((start_index, start_index + answer_length), start_score + end_score))
        lowerCAmelCase__					     = sorted(lowercase__      ,   key=lambda lowercase__: x[1]      ,   reverse=lowercase__)
        lowerCAmelCase__					     = []
        for (start_index, end_index), score in scores:
               if start_index > end_index:
                      raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""")
               lowerCAmelCase__					     = end_index - start_index + 1
               if length > max_answer_length:
                      raise ValueError(F"""Span is too long: {length} > {max_answer_length}""")
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals):
                      continue
               chosen_span_intervals.append((start_index, end_index))

               if len(lowercase__) == top_spans:
                      break
        return chosen_span_intervals






@add_end_docstrings(SCREAMING_SNAKE_CASE						)
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= VOCAB_FILES_NAMES
 UpperCAmelCase_     			= READER_PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_     			= READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_     			= READER_PRETRAINED_INIT_CONFIGURATION
 UpperCAmelCase_     			= ['input_ids', 'attention_mask']

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowerCAmelCase__     						=			{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__     						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}
       with open(lowerCAmelCase__     ,  'r'     ) as file:
              for line_number, line in enumerate(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = line.strip()
                     if line:
                            lowerCAmelCase__					     = line.split()
                            lowerCAmelCase__					     = line_number
                            lowerCAmelCase__					     = words[0]
                            lowerCAmelCase__					     = value
       return result






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for attribute in key.split('.'     ):
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     ).shape
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = hf_pointer
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = shape_pointer.shape

              # let's reduce dimension
              lowerCAmelCase__					     = value[0]
       else:
              lowerCAmelCase__					     = 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":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_g":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_v":
              lowerCAmelCase__					     = value
       elif weight_type == "bias":
              lowerCAmelCase__					     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       else:
              lowerCAmelCase__					     = value

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = '.'.join([key, weight_type]     )
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = '.'.join([key, hf_param_name]     )
       else:
              lowerCAmelCase__					     = key

       lowerCAmelCase__					     = value if 'lm_head' in full_key else value[0]


lowerCAmelCase__     						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = False
       for key, mapped_key in MAPPING.items():
              lowerCAmelCase__					     = 'wav2vec2.' + 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]:
                     lowerCAmelCase__					     = True
                     if "*" in mapped_key:
                            lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
                            lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
                     if "weight_g" in name:
                            lowerCAmelCase__					     = 'weight_g'
                     elif "weight_v" in name:
                            lowerCAmelCase__					     = 'weight_v'
                     elif "bias" in name:
                            lowerCAmelCase__					     = 'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            lowerCAmelCase__					     = 'weight'
                     else:
                            lowerCAmelCase__					     = None
                     if hf_dict is not None:
                            rename_dict(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     else:
                            set_recursively(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     return is_used
       return is_used






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = fairseq_model.state_dict()

       lowerCAmelCase__					     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCAmelCase__					     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
                     lowerCAmelCase__					     = True
              else:
                     lowerCAmelCase__					     = load_wavaveca_layer(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              if not is_used:
                     unused_weights.append(lowerCAmelCase__     )

       logger.warning(F"""Unused weights: {unused_weights}"""     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = full_name.split('conv_layers.'     )[-1]
       lowerCAmelCase__					     = name.split('.'     )
       lowerCAmelCase__					     = int(items[0]     )
       lowerCAmelCase__					     = 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."""     )
                     lowerCAmelCase__					     = 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."""     )
                     lowerCAmelCase__					     = 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
       else:
              unused_weights.append(lowerCAmelCase__     )






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ):
       if config_path is not None:
              lowerCAmelCase__					     = WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaConfig()

       if is_seq_class:
              lowerCAmelCase__					     = read_txt_into_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = WavaVecaForSequenceClassification(lowerCAmelCase__     )
              lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                  feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
              feature_extractor.save_pretrained(lowerCAmelCase__     )

       elif is_finetuned:
              if dict_path:
                     lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

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

                     # fairseq has the <pad> and <s> switched
                     lowerCAmelCase__					     = 0
                     lowerCAmelCase__					     = 1
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
                            json.dump(lowerCAmelCase__     ,  lowerCAmelCase__     )
                     lowerCAmelCase__					     = WavaVecaCTCTokenizer(
                         lowerCAmelCase__     ,  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=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = True if config.feat_extract_norm == 'layer' else False
                     lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                         feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
                     processor.save_pretrained(lowerCAmelCase__     )

              lowerCAmelCase__					     = WavaVecaForCTC(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaForPreTraining(lowerCAmelCase__     )

       if is_finetuned or is_seq_class:
              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
       else:
              lowerCAmelCase__					     = argparse.Namespace(task='audio_pretraining'     )
              lowerCAmelCase__					     = fairseq.tasks.setup_task(lowerCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowerCAmelCase__     )

       lowerCAmelCase__					     = model[0].eval()

       recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  not is_finetuned     )

       hf_wavavec.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			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'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    lowerCAmelCase__     						=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )
