



"""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:     Dict										=	{
    '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    _snake_case		(		UpperCamelCase		:	Optional[Any]		)     ->   Optional[int]:
 UpperCAmelCase			:      List[Any]			=			["""layers""", """blocks"""]
 for k in ignore_keys:
  state_dict.pop(lowercase_    ,	lowercase_		)


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    _snake_case		(		UpperCamelCase		:	str		)     ->   Any:
 UpperCAmelCase			:      Dict			=			list(s_dict.keys()		)
 for key in keys:
  UpperCAmelCase			:      List[str]			=			key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    UpperCAmelCase			:      List[Any]			=			new_key.replace(lowercase_    ,	lowercase_		)

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

  UpperCAmelCase			:      Tuple			=			s_dict.pop(lowercase_		)
 return s_dict

def    _snake_case		(		UpperCamelCase		:	Tuple		)     ->   Optional[int]:
 UpperCAmelCase			:      Any			=			emb.weight.shape
 UpperCAmelCase			:      str			=			nn.Linear(lowercase_    ,	lowercase_    ,	bias=lowercase_		)
 UpperCAmelCase			:      Union[str, Any]			=			emb.weight.data
 return lin_layer

def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	str		)     ->   bytes:
 os.makedirs(lowercase_    ,	exist_ok=lowercase_		)
 UpperCAmelCase			:      Tuple			=			os.path.basename(lowercase_		)

 UpperCAmelCase			:      int			=			url.split("""/"""		)[-2]
 UpperCAmelCase			:      Dict			=			os.path.join(lowercase_    ,	lowercase_		)

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

 if os.path.isfile(lowercase_		):
  UpperCAmelCase			:      Optional[Any]			=			open(lowercase_    ,	"""rb"""		).read()
  if hashlib.shaaaa(lowercase_		).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(lowercase_		) as source, open(lowercase_    ,	"""wb"""		) as output:
  with tqdm(
      total=int(source.info().get("""Content-Length"""		)		)    ,	ncols=80    ,	unit="""iB"""    ,	unit_scale=lowercase_    ,	unit_divisor=1024		) as loop:
   while True:
    UpperCAmelCase			:      Any			=			source.read(8192		)
    if not buffer:
     break

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

 UpperCAmelCase			:      Dict			=			open(lowercase_    ,	"""rb"""		).read()
 if hashlib.shaaaa(lowercase_		).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    _snake_case		(		UpperCamelCase		:	List[Any]    ,	UpperCamelCase		:	Tuple		)     ->   Optional[Any]:
 if ".pt" not in checkpoint_path:
  UpperCAmelCase			:      Tuple			=			_download(_MODELS[checkpoint_path]		)
 else:
  UpperCAmelCase			:      Optional[int]			=			torch.load(lowercase_    ,	map_location="""cpu"""		)
 UpperCAmelCase			:      str			=			original_checkpoint["""dims"""]
 UpperCAmelCase			:      List[Any]			=			original_checkpoint["""model_state_dict"""]
 UpperCAmelCase			:      Optional[Any]			=			state_dict["""decoder.token_embedding.weight"""]
 remove_ignore_keys_(lowercase_		)
 rename_keys(lowercase_		)
 UpperCAmelCase			:      List[str]			=			True
 UpperCAmelCase			:      Optional[Any]			=			state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

 UpperCAmelCase			:      List[Any]			=			WhisperConfig(
     vocab_size=dimensions["""n_vocab"""]    ,	encoder_ffn_dim=lowercase_    ,	decoder_ffn_dim=lowercase_    ,	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			:      Optional[Any]			=			WhisperForConditionalGeneration(lowercase_		)
 UpperCAmelCase			:      List[Any]			=			model.model.load_state_dict(lowercase_    ,	strict=lowercase_		)
 if len(lowercase_		) > 0 and not set(lowercase_		) <= {
     "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			:      Any			=			make_linear_from_emb(model.model.decoder.embed_tokens		)
 else:
  UpperCAmelCase			:      str			=			proj_out_weights

 model.save_pretrained(lowercase_		)


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:     Tuple										=	parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""
import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


A:     Optional[Any]										=	"\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",\n    author = \"Lin, Chin-Yew  and\n      Och, Franz Josef\",\n    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",\n    month = \"aug 23{--}aug 27\",\n    year = \"2004\",\n    address = \"Geneva, Switzerland\",\n    publisher = \"COLING\",\n    url = \"https://www.aclweb.org/anthology/C04-1072\",\n    pages = \"501--507\",\n}\n"

A:     Optional[int]										=	"\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,\nthe better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n"

A:     Optional[int]										=	"\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    'bleu': bleu score,\n    'precisions': geometric mean of n-gram precisions,\n    'brevity_penalty': brevity penalty,\n    'length_ratio': ratio of lengths,\n    'translation_length': translation_length,\n    'reference_length': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample\n    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)\n    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric(\"bleu\")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results[\"bleu\"])\n    1.0\n"



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

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,  id="""token"""		)   ,  id="""sequence"""		)   ,  id="""references"""		),
            }		)   ,  codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,  reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,  )




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE=4   ,  _SCREAMING_SNAKE_CASE=False		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Dict			=			compute_bleu(
        reference_corpus=_SCREAMING_SNAKE_CASE   ,  translation_corpus=_SCREAMING_SNAKE_CASE   ,  max_order=_SCREAMING_SNAKE_CASE   ,  smooth=_SCREAMING_SNAKE_CASE		)
    ((UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase)			,	(UpperCAmelCase))			:      Dict			=			score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
