from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
    import importlib_metadata
else:
    import importlib.metadata as importlib_metadata


__lowercase										= ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):


    class      lowerCamelCase_					(       tr.AbstractTransform			):






        '''simple docstring'''






        def __init__(     self			,			__lowercase = " ")	->     Optional[Any]:
            __UpperCamelCase					:Dict        =		sentence_delimiter







        def       UpperCamelCase__       (     self			,			__lowercase)	->     str:
            return list(__lowercase)







        def       UpperCamelCase__       (     self			,			__lowercase)	->     Any:
            __UpperCamelCase					:Union[str, Any]        =		[]
            for sent_idx, sentence in enumerate(__lowercase):
                chars.extend(self.process_string(__lowercase))
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowercase) - 1:
                    chars.append(self.sentence_delimiter)
            return chars
    __lowercase										= tr.Compose(
        [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
    )
else:
    __lowercase										= tr.Compose(
        [
            tr.RemoveMultipleSpaces(),
            tr.Strip(),
            tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
            tr.ReduceToListOfListOfChars(),
        ]
    )


__lowercase										= '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

__lowercase										= '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

__lowercase										= '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''


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






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Tuple:
        return datasets.MetricInfo(
            description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
                {
                    '''predictions''': datasets.Value('''string'''			,			id='''sequence'''),
                    '''references''': datasets.Value('''string'''			,			id='''sequence'''),
                })			,			codebase_urls=['''https://github.com/jitsi/jiwer/''']			,			reference_urls=[
                '''https://en.wikipedia.org/wiki/Word_error_rate''',
                '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
            ]			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase=False)	->     Optional[Any]:
        if concatenate_texts:
            return jiwer.compute_measures(
                __lowercase			,			__lowercase			,			truth_transform=__lowercase			,			hypothesis_transform=__lowercase			,			)["wer"]

        __UpperCamelCase					:int        =		0
        __UpperCamelCase					:Dict        =		0
        for prediction, reference in zip(__lowercase			,			__lowercase):
            __UpperCamelCase					:List[Any]        =		jiwer.compute_measures(
                __lowercase			,			__lowercase			,			truth_transform=__lowercase			,			hypothesis_transform=__lowercase			,			)
            incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
            total += measures["substitutions"] + measures["deletions"] + measures["hits"]

        return incorrect / total




import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


__lowercase										= {
    '''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
    '''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
    '''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
    '''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
    '''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
    '''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
    '''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
    '''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
    '''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
    '''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )


__lowercase										= {
    '''blocks''': '''layers''',
    '''mlp.0''': '''fc1''',
    '''mlp.2''': '''fc2''',
    '''mlp_ln''': '''final_layer_norm''',
    '''.attn.query''': '''.self_attn.q_proj''',
    '''.attn.key''': '''.self_attn.k_proj''',
    '''.attn.value''': '''.self_attn.v_proj''',
    '''.attn_ln''': '''.self_attn_layer_norm''',
    '''.attn.out''': '''.self_attn.out_proj''',
    '''.cross_attn.query''': '''.encoder_attn.q_proj''',
    '''.cross_attn.key''': '''.encoder_attn.k_proj''',
    '''.cross_attn.value''': '''.encoder_attn.v_proj''',
    '''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
    '''.cross_attn.out''': '''.encoder_attn.out_proj''',
    '''decoder.ln.''': '''decoder.layer_norm.''',
    '''encoder.ln.''': '''encoder.layer_norm.''',
    '''token_embedding''': '''embed_tokens''',
    '''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
    '''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
    '''ln_post''': '''layer_norm''',
}



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		list(s_dict.keys()     )
    for key in keys:
        __UpperCamelCase					:Dict        =		key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __UpperCamelCase					:str        =		new_key.replace(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        print(f"""{key} -> {new_key}"""     )

        __UpperCamelCase					:Any        =		s_dict.pop(SCREAMING_SNAKE_CASE     )
    return s_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		emb.weight.shape
    __UpperCamelCase					:Any        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:str        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    os.makedirs(SCREAMING_SNAKE_CASE  ,      exist_ok=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:int        =		os.path.basename(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Optional[Any]        =		url.split('''/'''     )[-2]
    __UpperCamelCase					:Tuple        =		os.path.join(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

    if os.path.exists(SCREAMING_SNAKE_CASE     ) and not os.path.isfile(SCREAMING_SNAKE_CASE     ):
        raise RuntimeError(f"""{download_target} exists and is not a regular file"""     )

    if os.path.isfile(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:List[str]        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
        if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).hexdigest() == expected_shaaaa:
            return model_bytes
        else:
            warnings.warn(f"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""     )

    with urllib.request.urlopen(SCREAMING_SNAKE_CASE     ) as source, open(SCREAMING_SNAKE_CASE  ,      '''wb'''     ) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''     )     )  ,      ncols=80  ,      unit='''iB'''  ,      unit_scale=SCREAMING_SNAKE_CASE  ,      unit_divisor=1_024     ) as loop:
            while True:
                __UpperCamelCase					:Optional[Any]        =		source.read(8_192     )
                if not buffer:
                    break

                output.write(SCREAMING_SNAKE_CASE     )
                loop.update(len(SCREAMING_SNAKE_CASE     )     )

    __UpperCamelCase					:str        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
    if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).hexdigest() != expected_shaaaa:
        raise RuntimeError(
            '''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'''     )

    return model_bytes



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if ".pt" not in checkpoint_path:
        __UpperCamelCase					:Tuple        =		_download(_MODELS[checkpoint_path]     )
    else:
        __UpperCamelCase					:Optional[int]        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Union[str, Any]        =		original_checkpoint['''dims''']
    __UpperCamelCase					:List[Any]        =		original_checkpoint['''model_state_dict''']
    __UpperCamelCase					:Optional[Any]        =		state_dict['''decoder.token_embedding.weight''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    rename_keys(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		True
    __UpperCamelCase					:Tuple        =		state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		WhisperConfig(
        vocab_size=dimensions['''n_vocab''']  ,      encoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      decoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      num_mel_bins=dimensions['''n_mels''']  ,      d_model=dimensions['''n_audio_state''']  ,      max_target_positions=dimensions['''n_text_ctx''']  ,      encoder_layers=dimensions['''n_audio_layer''']  ,      encoder_attention_heads=dimensions['''n_audio_head''']  ,      decoder_layers=dimensions['''n_text_layer''']  ,      decoder_attention_heads=dimensions['''n_text_state''']  ,      max_source_positions=dimensions['''n_audio_ctx''']  ,      )

    __UpperCamelCase					:str        =		WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase  ,	__UpperCamelCase					:Any        =		model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
            f""" but all the following weights are missing {missing}"""     )

    if tie_embeds:
        __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.decoder.embed_tokens     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		proj_out_weights

    model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # # Required parameters
    parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    __lowercase										= parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





