'''simple docstring'''

import re
import string

import numpy as np

import datasets


__a:					List[Any]			  =  """\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"""

__a:					Dict			  =  """\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"""

__a:					Dict			  =  """\n"""



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





       '''simple docstring'''



       def _lowerCAmelCase(		self					)    ->    Any:
        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'''					),
                }					)      ,  reference_urls=[]      ,  )


       def _lowerCAmelCase(		self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=False      ,  )    ->    List[Any]:


        if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
          lowercase__							:       Dict										=   np.array([re.sub(snake_case_      ,  ''''''      ,  snake_case_					) for x in predictions]					)
          lowercase__							:       Tuple										=   np.array([re.sub(snake_case_      ,  ''''''      ,  snake_case_					) for x in references]					)
        else:
         lowercase__							:       List[Any]										=   np.asarray(snake_case_					)
         lowercase__							:       Optional[Any]										=   np.asarray(snake_case_					)

        if ignore_case:
         lowercase__							:       Tuple										=   np.char.lower(snake_case_					)
         lowercase__							:       Dict										=   np.char.lower(snake_case_					)

        if ignore_punctuation:
         lowercase__							:       Optional[Any]										=   string.punctuation.maketrans(''''''      ,  ''''''      ,  string.punctuation					)
         lowercase__							:       Any										=   np.char.translate(snake_case_      ,  table=snake_case_					)
         lowercase__							:       Optional[Any]										=   np.char.translate(snake_case_      ,  table=snake_case_					)

        if ignore_numbers:
         lowercase__							:       Dict										=   string.digits.maketrans(''''''      ,  ''''''      ,  string.digits					)
         lowercase__							:       List[str]										=   np.char.translate(snake_case_      ,  table=snake_case_					)
         lowercase__							:       str										=   np.char.translate(snake_case_      ,  table=snake_case_					)

        lowercase__							:       Any										=   predictions == references

        return {"exact_match": np.mean(snake_case_					) * 100}


"""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


_UpperCamelCase								=		{
    "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  _A(		lowerCAmelCase							):
							A__       :     Any								=			["""layers""", """blocks"""]
							for k in ignore_keys:
														state_dict.pop(lowerCAmelCase			,  lowerCAmelCase							)


_UpperCamelCase								=		{
    "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  _A(		lowerCAmelCase							):
							A__       :     str								=			list(s_dict.keys()							)
							for key in keys:
														A__       :     Optional[Any]								=			key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												A__       :     Optional[Any]								=			new_key.replace(lowerCAmelCase			,  lowerCAmelCase							)

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

														A__       :     Any								=			s_dict.pop(lowerCAmelCase							)
							return s_dict

def  _A(		lowerCAmelCase							):
							A__				,							A__       :     Union[str, Any]								=			emb.weight.shape
							A__       :     Optional[Any]								=			nn.Linear(lowerCAmelCase			,  lowerCAmelCase			,  bias=lowerCAmelCase							)
							A__       :     int								=			emb.weight.data
							return lin_layer

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							os.makedirs(lowerCAmelCase			,  exist_ok=lowerCAmelCase							)
							A__       :     Union[str, Any]								=			os.path.basename(lowerCAmelCase							)

							A__       :     str								=			url.split("""/"""							)[-2]
							A__       :     Union[str, Any]								=			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							):
														A__       :     Tuple								=			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=1024							) as loop:
																					while True:
																												A__       :     Dict								=			source.read(8192							)
																												if not buffer:
																																			break

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

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

							A__       :     str								=			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"""]			,  )

							A__       :     int								=			WhisperForConditionalGeneration(lowerCAmelCase							)
							A__				,							A__       :     List[Any]								=			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:
														A__       :     Tuple								=			make_linear_from_emb(model.model.decoder.embed_tokens							)
							else:
														A__       :     Any								=			proj_out_weights

							model.save_pretrained(lowerCAmelCase							)


if __name__ == "__main__":
				_UpperCamelCase								=		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.")
				_UpperCamelCase								=		parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





