





'''simple docstring'''






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


A__				:   int      ={
    '''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    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							['''layers''', '''blocks''']
      for k in ignore_keys:
            state_dict.pop(__lowerCamelCase							,	__lowerCamelCase  )


A__				:   Any      ={
    '''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    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							list(s_dict.keys()  )
      for key in keys:
            _lowerCAmelCase							     =							key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        _lowerCAmelCase							     =							new_key.replace(__lowerCamelCase							,	__lowerCamelCase  )

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

            _lowerCAmelCase							     =							s_dict.pop(__lowerCamelCase  )
      return s_dict
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							emb.weight.shape
      _lowerCAmelCase							     =							nn.Linear(__lowerCamelCase							,	__lowerCamelCase							,	bias=__lowerCamelCase  )
      _lowerCAmelCase							     =							emb.weight.data
      return lin_layer
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      os.makedirs(__lowerCamelCase							,	exist_ok=__lowerCamelCase  )
      _lowerCAmelCase							     =							os.path.basename(__lowerCamelCase  )

      _lowerCAmelCase							     =							url.split("""/"""  )[-2]
      _lowerCAmelCase							     =							os.path.join(__lowerCamelCase							,	__lowerCamelCase  )

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

      if os.path.isfile(__lowerCamelCase  ):
            _lowerCAmelCase							     =							open(__lowerCamelCase							,	"""rb"""  ).read()
            if hashlib.shaaaa(__lowerCamelCase  ).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(__lowerCamelCase  ) as source, open(__lowerCamelCase							,	"""wb"""  ) as output:
            with tqdm(
                total=int(source.info().get("""Content-Length"""  )  )							,	ncols=80							,	unit="""iB"""							,	unit_scale=__lowerCamelCase							,	unit_divisor=10_24  ) as loop:
                  while True:
                        _lowerCAmelCase							     =							source.read(81_92  )
                        if not buffer:
                              break

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

      _lowerCAmelCase							     =							open(__lowerCamelCase							,	"""rb"""  ).read()
      if hashlib.shaaaa(__lowerCamelCase  ).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    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      if ".pt" not in checkpoint_path:
            _lowerCAmelCase							     =							_download(_MODELS[checkpoint_path]  )
      else:
            _lowerCAmelCase							     =							torch.load(__lowerCamelCase							,	map_location="""cpu"""  )
      _lowerCAmelCase							     =							original_checkpoint['''dims''']
      _lowerCAmelCase							     =							original_checkpoint['''model_state_dict''']
      _lowerCAmelCase							     =							state_dict['''decoder.token_embedding.weight''']
      remove_ignore_keys_(__lowerCamelCase  )
      rename_keys(__lowerCamelCase  )
      _lowerCAmelCase							     =							True
      _lowerCAmelCase							     =							state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

      _lowerCAmelCase							     =							WhisperConfig(
          vocab_size=dimensions["""n_vocab"""]							,	encoder_ffn_dim=__lowerCamelCase							,	decoder_ffn_dim=__lowerCamelCase							,	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"""]							,	)

      _lowerCAmelCase							     =							WhisperForConditionalGeneration(__lowerCamelCase  )
      _lowerCAmelCase							     =							model.model.load_state_dict(__lowerCamelCase							,	strict=__lowerCamelCase  )
      if len(__lowerCamelCase  ) > 0 and not set(__lowerCamelCase  ) <= {
          "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:
            _lowerCAmelCase							     =							make_linear_from_emb(model.model.decoder.embed_tokens  )
      else:
            _lowerCAmelCase							     =							proj_out_weights

      model.save_pretrained(__lowerCamelCase  )


if __name__ == "__main__":
      A__				:   Any      =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.''')
      A__				:   List[Any]      =parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)











"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase_   						=   '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase_   						=   '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION    )
class        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']			,reference_urls=[
				        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
				        '''https://en.wikipedia.org/wiki/METEOR''',
				    ]			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				import nltk

				nltk.download('''wordnet'''						)
				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						nltk.download('''punkt'''						)
				if NLTK_VERSION >= version.Version('''3.6.6'''						):
						nltk.download('''omw-1.4'''						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

				return {"meteor": np.mean(_snake_case						)}
