





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


UpperCAmelCase_     							= logging.get_logger(__name__)

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

UpperCAmelCase_     							= {
    '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'
        ),
    },
}
UpperCAmelCase_     							= {
    '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'
        ),
    },
}
UpperCAmelCase_     							= {
    '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'
        ),
    },
}

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


UpperCAmelCase_     							= {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
UpperCAmelCase_     							= {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
UpperCAmelCase_     							= {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							int   		=		VOCAB_FILES_NAMES
   a						:							Any   		=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   a						:							Optional[int]   		=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							Any   		=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		VOCAB_FILES_NAMES
   a						:							str   		=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
   a						:							Any   		=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							Optional[int]   		=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




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

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


UpperCAmelCase_     							= 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(__lowerCamelCase				)
class     lowercase__       :

   '''simple docstring'''


   def __call__(    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = False,		__magic_name__ = False,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = None,		**__magic_name__,		) ->      BatchEncoding:






       """simple docstring"""
       if titles is None and texts is None:
           return super().__call__(
               __magic_name__,		padding=__magic_name__,		truncation=__magic_name__,		max_length=__magic_name__,		return_tensors=__magic_name__,		return_attention_mask=__magic_name__,		**__magic_name__,		)
       elif titles is None or texts is None:
           UpperCamelCase__							:				Optional[Any]          =      titles if texts is None else texts
           return super().__call__(
               __magic_name__,		__magic_name__,		padding=__magic_name__,		truncation=__magic_name__,		max_length=__magic_name__,		return_tensors=__magic_name__,		return_attention_mask=__magic_name__,		**__magic_name__,		)
       UpperCamelCase__							:				Optional[int]          =      titles if not isinstance(__magic_name__,		__magic_name__							) else [titles]
       UpperCamelCase__							:				Dict          =      texts if not isinstance(__magic_name__,		__magic_name__							) else [texts]
       UpperCamelCase__							:				Optional[Any]          =      len(__magic_name__							)
       UpperCamelCase__							:				Dict          =      questions if not isinstance(__magic_name__,		__magic_name__							) else [questions] * n_passages
       if len(__magic_name__							) != len(__magic_name__							):
           raise ValueError(
               f"There should be as many titles than texts but got {len(__magic_name__							)} titles and {len(__magic_name__							)} texts."							)
       UpperCamelCase__							:				Any          =      super().__call__(__magic_name__,		__magic_name__,		padding=__magic_name__,		truncation=__magic_name__							)['''input_ids''']
       UpperCamelCase__							:				Optional[int]          =      super().__call__(__magic_name__,		add_special_tokens=__magic_name__,		padding=__magic_name__,		truncation=__magic_name__							)['''input_ids''']
       UpperCamelCase__							:				Union[str, Any]          =      {
           '''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(__magic_name__,		__magic_name__							)
           ]
       }
       if return_attention_mask is not False:
           UpperCamelCase__							:				Any          =      []
           for input_ids in encoded_inputs["input_ids"]:
               attention_mask.append([int(input_id != self.pad_token_id							) for input_id in input_ids]							)
           UpperCamelCase__							:				Union[str, Any]          =      attention_mask
       return self.pad(__magic_name__,		padding=__magic_name__,		max_length=__magic_name__,		return_tensors=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__ = 16,		__magic_name__ = 64,		__magic_name__ = 4,		) ->      List[DPRSpanPrediction]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      reader_input['''input_ids''']
       UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Any          =      reader_output[:3]
       UpperCamelCase__							:				Tuple          =      len(__magic_name__							)
       UpperCamelCase__							:				int          =      sorted(range(__magic_name__							),		reverse=__magic_name__,		key=relevance_logits.__getitem__							)
       UpperCamelCase__							:				List[DPRReaderOutput]          =      []
       for doc_id in sorted_docs:
           UpperCamelCase__							:				Optional[int]          =      list(input_ids[doc_id]							)
           # assuming question & title information is at the beginning of the sequence
           UpperCamelCase__							:				Optional[Any]          =      sequence_ids.index(self.sep_token_id,		2							) + 1  # second sep id
           if sequence_ids[-1] == self.pad_token_id:
               UpperCamelCase__							:				Tuple          =      sequence_ids.index(self.pad_token_id							)
           else:
               UpperCamelCase__							:				int          =      len(__magic_name__							)

           UpperCamelCase__							:				Union[str, Any]          =      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=__magic_name__,		top_spans=__magic_name__,		)
           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=__magic_name__,		start_index=__magic_name__,		end_index=__magic_name__,		text=self.decode(sequence_ids[start_index : end_index + 1]							),		)							)
           if len(__magic_name__							) >= num_spans:
               break
       return nbest_spans_predictions[:num_spans]




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      List[DPRSpanPrediction]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      []
       for start_index, start_score in enumerate(__magic_name__							):
           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)							)
       UpperCamelCase__							:				List[Any]          =      sorted(__magic_name__,		key=lambda __magic_name__							: x[1],		reverse=__magic_name__							)
       UpperCamelCase__							:				int          =      []
       for (start_index, end_index), score in scores:
           if start_index > end_index:
               raise ValueError(f"Wrong span indices: [{start_index}:{end_index}]"							)
           UpperCamelCase__							:				str          =      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(__magic_name__							) == top_spans:
               break
       return chosen_span_intervals






@add_end_docstrings(__lowerCamelCase				)
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Tuple   		=		VOCAB_FILES_NAMES
   a						:							Union[str, Any]   		=		READER_PRETRAINED_VOCAB_FILES_MAP
   a						:							str   		=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							Optional[int]   		=		READER_PRETRAINED_INIT_CONFIGURATION
   a						:							List[Any]   		=		["input_ids", "attention_mask"]








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()
UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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',
}
UpperCAmelCase_     							= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Any      )      ->	List[str]:
    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
                UpperCamelCase__							:				Tuple          =      '''lm_head'''

        UpperCamelCase__							:				Optional[int]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      )

    if weight_type is not None:
        UpperCamelCase__							:				List[str]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      ).shape
    else:
        UpperCamelCase__							:				Optional[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":
        UpperCamelCase__							:				List[Any]          =      value
    elif weight_type == "weight_g":
        UpperCamelCase__							:				List[str]          =      value
    elif weight_type == "weight_v":
        UpperCamelCase__							:				str          =      value
    elif weight_type == "bias":
        UpperCamelCase__							:				Tuple          =      value
    else:
        UpperCamelCase__							:				int          =      value

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	List[Any]:
    UpperCamelCase__							:				Optional[Any]          =      []
    UpperCamelCase__							:				str          =      fairseq_model.state_dict()

    UpperCamelCase__							:				Optional[int]          =      hf_model.unispeech.feature_extractor

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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple      )      ->	Optional[int]:
    UpperCamelCase__							:				List[str]          =      full_name.split('''conv_layers.'''      )[-1]
    UpperCamelCase__							:				List[str]          =      name.split('''.'''      )
    UpperCamelCase__							:				str          =      int(items[0]      )
    UpperCamelCase__							:				Union[str, 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."
            )
            UpperCamelCase__							:				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."
            )
            UpperCamelCase__							:				Tuple          =      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__							:				int          =      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__							:				List[str]          =      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(__UpperCAmelCase      )




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict=None		,   __UpperCAmelCase:			Optional[Any]=None		,   __UpperCAmelCase:			Optional[int]=True      )      ->	Union[str, Any]:
    if config_path is not None:
        UpperCamelCase__							:				str          =      UniSpeechConfig.from_pretrained(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				List[Any]          =      UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            UpperCamelCase__							:				str          =      Dictionary.load_from_json(__UpperCAmelCase      )

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

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__							:				Optional[Any]          =      42
            UpperCamelCase__							:				List[str]          =      43
            with open(__UpperCAmelCase		,   '''w'''		,   encoding='''utf-8'''      ) as vocab_handle:
                json.dump(__UpperCAmelCase		,   __UpperCAmelCase      )
            UpperCamelCase__							:				Dict          =      WavaVecaPhonemeCTCTokenizer(
                __UpperCAmelCase		,   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=__UpperCAmelCase		,   )
            UpperCamelCase__							:				List[Any]          =      True if config.feat_extract_norm == '''layer''' else False
            UpperCamelCase__							:				str          =      WavaVecaFeatureExtractor(
                feature_size=1		,   sampling_rate=1_6000		,   padding_value=0		,   do_normalize=__UpperCAmelCase		,   return_attention_mask=__UpperCAmelCase		,   )
            UpperCamelCase__							:				str          =      WavaVecaProcessor(feature_extractor=__UpperCAmelCase		,   tokenizer=__UpperCAmelCase      )
            processor.save_pretrained(__UpperCAmelCase      )

        UpperCamelCase__							:				Optional[int]          =      UniSpeechForCTC(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				Any          =      UniSpeechForPreTraining(__UpperCAmelCase      )

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

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

    recursively_load_weights(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )

    hf_unispeech.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 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'
  )
  UpperCAmelCase_     							= parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )

