'''simple docstring'''





import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


SCREAMING_SNAKE_CASE_:					Tuple  =datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					Union[str, Any]  ='\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = "Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric",\n    author = "Moosavi, Nafise Sadat  and\n      Strube, Michael",\n    booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",\n    month = aug,\n    year = "2016",\n    address = "Berlin, Germany",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/P16-1060",\n    doi = "10.18653/v1/P16-1060",\n    pages = "632--642",\n}\n\n'

SCREAMING_SNAKE_CASE_:					int  ='\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the "([pos] [word])" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a "-"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section "*_conll File Format"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n'

SCREAMING_SNAKE_CASE_:					Any  ='\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting \'keep_singletons=False\', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the \'NP_only\' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting \'min_span\', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    \'mentions\': mentions\n    \'muc\': MUC metric [Vilain et al, 1995]\n    \'bcub\': B-cubed [Bagga and Baldwin, 1998]\n    \'ceafe\': CEAFe [Luo et al., 2005]\n    \'lea\': LEA [Moosavi and Strube, 2016]\n    \'conll_score\': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric(\'coval\')\n    >>> words = [\'bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)\',\n    ... \'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -\',\n    ... \'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -\',\n    ... \'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -\']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {\'mentions/recall\': 1.0,[...] \'conll_score\': 100.0}\n'





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[Any] ,   snake_case_	:			Any=False ,   snake_case_	:			List[str]=False ,   snake_case_	:			List[str]=True ,   snake_case_	:			Union[str, Any]=False ,   snake_case_	:			int="dummy_doc"      )   ->				List[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       {doc: key_lines}
     UpperCAmelCase_	   =       {doc: sys_lines}

     UpperCAmelCase_	   =       {}

     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0

     UpperCAmelCase_   ,			UpperCAmelCase_	   =       reader.get_doc_mentions(snake_case_ ,   key_doc_lines[doc] ,   snake_case_      )
     key_singletons_num += singletons_num

     if NP_only or min_span:
          UpperCAmelCase_	   =       reader.set_annotated_parse_trees(snake_case_ ,   key_doc_lines[doc] ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_   ,			UpperCAmelCase_	   =       reader.get_doc_mentions(snake_case_ ,   sys_doc_lines[doc] ,   snake_case_      )
     sys_singletons_num += singletons_num

     if NP_only or min_span:
          UpperCAmelCase_	   =       reader.set_annotated_parse_trees(snake_case_ ,   key_doc_lines[doc] ,   snake_case_ ,   snake_case_      )

     if remove_nested:
          UpperCAmelCase_   ,			UpperCAmelCase_	   =       reader.remove_nested_coref_mentions(snake_case_ ,   snake_case_      )
          key_nested_coref_num += nested_mentions
          key_removed_nested_clusters += removed_clusters

          UpperCAmelCase_   ,			UpperCAmelCase_	   =       reader.remove_nested_coref_mentions(snake_case_ ,   snake_case_      )
          sys_nested_coref_num += nested_mentions
          sys_removed_nested_clusters += removed_clusters

     UpperCAmelCase_	   =       reader.get_mention_assignments(snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       reader.get_mention_assignments(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       (key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

     if remove_nested:
          logger.info(
              "Number of removed nested coreferring mentions in the key "
              f"""annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}"""      )
          logger.info(
              "Number of resulting singleton clusters in the key "
              f"""annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}"""      )

     if not keep_singletons:
          logger.info(
              f"""{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system """
              "files, respectively"      )

     return doc_coref_infos





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Optional[int] ,   snake_case_	:			Dict ,   snake_case_	:			List[Any] ,   snake_case_	:			List[Any] ,   snake_case_	:			Dict ,   snake_case_	:			Any      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       get_coref_infos(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       {}
     UpperCAmelCase_	   =       0
     UpperCAmelCase_	   =       0

     for name, metric in metrics:
          UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       evaluator.evaluate_documents(snake_case_ ,   snake_case_ ,   beta=1      )
          if name in ["muc", "bcub", "ceafe"]:
               conll += fa
               conll_subparts_num += 1
          output_scores.update({f"""{name}/recall""": recall, f"""{name}/precision""": precision, f"""{name}/f1""": fa}      )

          logger.info(
              name.ljust(10      ) ,   f"""Recall: {recall * 1_00:.2f}""" ,   f""" Precision: {precision * 1_00:.2f}""" ,   f""" F1: {fa * 1_00:.2f}""" ,   )

     if conll_subparts_num == 3:
          UpperCAmelCase_	   =       (conll / 3) * 1_00
          logger.info(f"""CoNLL score: {conll:.2f}"""      )
          output_scores.update({"conll_score": conll}      )

     return output_scores





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       False
     for line in key_lines:
          if not line.startswith("#"      ):
               if len(line.split()      ) > 6:
                    UpperCAmelCase_	   =       line.split()[5]
                    if not parse_col == "-":
                         UpperCAmelCase_	   =       True
                         break
                    else:
                         break
     return has_gold_parse






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,     _KWARGS_DESCRIPTION     )
class      __A				(			datasets.Metric     ):



    def  _lowercase						(self   :   Optional[int]			):
         return datasets.MetricInfo(
             description=_DESCRIPTION       ,      citation=_CITATION       ,      inputs_description=_KWARGS_DESCRIPTION       ,      features=datasets.Features(
                 {
                     "predictions": datasets.Sequence(datasets.Value("string"			)			),
                     "references": datasets.Sequence(datasets.Value("string"			)			),
                 }			)       ,      codebase_urls=["https://github.com/ns-moosavi/coval"]       ,      reference_urls=[
                 "https://github.com/ns-moosavi/coval",
                 "https://www.aclweb.org/anthology/P16-1060",
                 "http://www.conll.cemantix.org/2012/data.html",
             ]       ,      )






    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[int]       ,      __a   :   Union[str, Any]       ,      __a   :   int=True       ,      __a   :   List[Any]=False       ,      __a   :   Optional[Any]=False       ,      __a   :   List[str]=False			):
         UpperCAmelCase_	   =       [
             ("mentions", evaluator.mentions),
             ("muc", evaluator.muc),
             ("bcub", evaluator.b_cubed),
             ("ceafe", evaluator.ceafe),
             ("lea", evaluator.lea),
         ]

         if min_span:
              UpperCAmelCase_	   =       util.check_gold_parse_annotation(__a			)
              if not has_gold_parse:
                   raise NotImplementedError("References should have gold parse annotation to use 'min_span'."			)
                   # util.parse_key_file(key_file)
                   # key_file = key_file + ".parsed"

         UpperCAmelCase_	   =       evaluate(
             key_lines=__a       ,      sys_lines=__a       ,      metrics=__a       ,      NP_only=__a       ,      remove_nested=__a       ,      keep_singletons=__a       ,      min_span=__a       ,      )

         return score





'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					int  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Dict  ={
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_:					List[str]  =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			List[Any] ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			int ,   snake_case_	:			Any      )   ->				Optional[int]:


     '''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_	:			List[Any] ,   snake_case_	:			List[str]      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       []
     UpperCAmelCase_	   =       fairseq_model.state_dict()

     UpperCAmelCase_	   =       hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     UpperCAmelCase_	   =       None

     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
          elif name.split("."      )[0] == "proj":
               UpperCAmelCase_	   =       fairseq_model.proj
               UpperCAmelCase_	   =       True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                         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 "bias" in name:
                              UpperCAmelCase_	   =       "bias"
                         elif "weight" in name:
                              UpperCAmelCase_	   =       "weight"
                         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}"""      )

     return proj_weight





def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			List[Any] ,   snake_case_	:			str ,   snake_case_	:			Any ,   snake_case_	:			int      )   ->				Dict:


     '''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_      )





def      lowerCAmelCase_    ( snake_case_	:			Any      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       emb.weight.shape
     UpperCAmelCase_	   =       nn.Linear(snake_case_ ,   snake_case_ ,   bias=snake_case_      )
     UpperCAmelCase_	   =       emb.weight.data
     return lin_layer





def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				List[str]:


     '''simple docstring'''




     with open(snake_case_ ,   "r" ,   encoding="utf-8"      ) as f:
          UpperCAmelCase_	   =       f.readlines()
          UpperCAmelCase_	   =       [line.split(" "      )[0] for line in lines]

     UpperCAmelCase_	   =       len(snake_case_      )

     UpperCAmelCase_	   =       {
         "<s>": 0,
         "<pad>": 1,
         "</s>": 2,
         "<unk>": 3,
     }

     vocab_dict.update(dict(zip(snake_case_ ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict





@torch.no_grad()
def      lowerCAmelCase_    ( snake_case_	:			Dict ,   snake_case_	:			Tuple ,   snake_case_	:			List[str] ,   snake_case_	:			Dict ,   snake_case_	:			Union[str, Any] ,   snake_case_	:			Tuple ,   snake_case_	:			Any ,   )   ->				Dict:


     '''simple docstring'''




     UpperCAmelCase_	   =       WavaVecaConfig.from_pretrained(snake_case_      )
     UpperCAmelCase_	   =       SpeechaTextaConfig.from_pretrained(
         snake_case_ ,   vocab_size=snake_case_ ,   decoder_layers=snake_case_ ,   do_stable_layer_norm=snake_case_      )

     UpperCAmelCase_	   =       WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_60_00 ,   padding_value=0 ,   do_normalize=snake_case_ ,   return_attention_mask=snake_case_ ,   )

     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
     UpperCAmelCase_	   =       model[0].eval()

     # set weights for wav2vec2 encoder
     UpperCAmelCase_	   =       WavaVecaModel(snake_case_      )
     UpperCAmelCase_	   =       recursively_load_weights_wavaveca(model.encoder ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaForCausalLM(snake_case_      )
     UpperCAmelCase_   ,			UpperCAmelCase_	   =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=snake_case_      )

     # set output linear layer
     unexpected_keys.remove("embed_out"      )
     UpperCAmelCase_	   =       nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
     logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

     UpperCAmelCase_	   =       SpeechEncoderDecoderModel(encoder=snake_case_ ,   decoder=snake_case_      )
     UpperCAmelCase_	   =       False

     # add projection layer
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.weight      )
     UpperCAmelCase_	   =       nn.Parameter(projection_layer.bias      )

     UpperCAmelCase_	   =       create_vocab_dict(snake_case_      )

     with open(os.path.join(snake_case_ ,   "vocab.json"      ) ,   "w"      ) as fp:
          json.dump(snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       SpeechaTextaTokenizer(os.path.join(snake_case_ ,   "vocab.json"      )      )
     tokenizer.save_pretrained(snake_case_      )

     UpperCAmelCase_	   =       hf_wavavec.config.to_dict()
     UpperCAmelCase_	   =       tokenizer.pad_token_id
     UpperCAmelCase_	   =       tokenizer.bos_token_id
     UpperCAmelCase_	   =       tokenizer.eos_token_id
     UpperCAmelCase_	   =       "speech_to_text_2"
     UpperCAmelCase_	   =       "wav2vec2"

     UpperCAmelCase_	   =       SpeechEncoderDecoderConfig.from_dict(snake_case_      )

     hf_wavavec.save_pretrained(snake_case_      )
     feature_extractor.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(
        '--encoder_config_path',
        default='facebook/wav2vec2-large-lv60',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/s2t-small-mustc-en-fr-st',
        type=str,
        help='Path to hf decoder s2t checkpoint config',
    )
    parser.add_argument('--vocab_size', default=1_02_24, type=int, help='Vocab size of decoder')
    parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

    SCREAMING_SNAKE_CASE_:					Dict  =parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )




