"""simple docstring"""



from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Any					     =				['input_features', 'attention_mask']





			def __init__(			self   ,      snake_case_=80   ,      snake_case_=16_000   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=25   ,      snake_case_="hamming_window"   ,      snake_case_=3_27_68.0   ,      snake_case_=0.97   ,      snake_case_=1.0   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_=False   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(feature_size=snake_case_   ,      sampling_rate=snake_case_   ,      padding_value=snake_case_   ,      **snake_case_							)

										A__       :     List[str]								=			feature_size
										A__       :     str								=			sampling_rate
										A__       :     List[str]								=			padding_value
										A__       :     List[str]								=			hop_length
										A__       :     Union[str, Any]								=			win_length
										A__       :     Optional[int]								=			frame_signal_scale
										A__       :     List[Any]								=			preemphasis_coeff
										A__       :     Tuple								=			mel_floor
										A__       :     Union[str, Any]								=			normalize_means
										A__       :     str								=			normalize_vars
										A__       :     int								=			win_function
										A__       :     List[Any]								=			return_attention_mask

										A__       :     Dict								=			win_length * sampling_rate // 1_000
										A__       :     List[Any]								=			hop_length * sampling_rate // 1_000

										A__       :     List[str]								=			optimal_fft_length(self.sample_size							)
										A__       :     Tuple								=			(self.n_fft // 2) + 1





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										if self.win_function == "hamming_window":
																	A__       :     Optional[int]								=			window_function(window_length=self.sample_size   ,      name=self.win_function   ,      periodic=snake_case_							)
										else:
																	A__       :     Dict								=			window_function(window_length=self.sample_size   ,      name=self.win_function							)

										A__       :     Optional[int]								=			mel_filter_bank(
										    num_frequency_bins=self.n_freqs   ,      num_mel_filters=self.feature_size   ,      min_frequency=0.0   ,      max_frequency=self.sampling_rate / 2.0   ,      sampling_rate=self.sampling_rate   ,      )

										A__       :     Any								=			spectrogram(
										    one_waveform * self.frame_signal_scale   ,      window=snake_case_   ,      frame_length=self.sample_size   ,      hop_length=self.sample_stride   ,      fft_length=self.n_fft   ,      center=snake_case_   ,      preemphasis=self.preemphasis_coeff   ,      mel_filters=snake_case_   ,      mel_floor=self.mel_floor   ,      log_mel="""log"""   ,      )
										return msfc_features.T





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_   ,      snake_case_							):







										'''simple docstring'''


										if self.normalize_means:
																	A__       :     str								=			x[:input_length].mean(axis=0							)
																	A__       :     List[Any]								=			np.subtract(snake_case_   ,      snake_case_							)
										if self.normalize_vars:
																	A__       :     str								=			x[:input_length].std(axis=0							)
																	A__       :     str								=			np.divide(snake_case_   ,      snake_case_							)

										if input_length < x.shape[0]:
																	A__       :     str								=			padding_value

										# make sure array is in float32
										A__       :     Dict								=			x.astype(np.floataa							)

										return x





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     List[str]								=			attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
										return [self._normalize_one(snake_case_   ,      snake_case_   ,      self.padding_value							) for x, n in zip(snake_case_   ,      snake_case_							)]





			def __call__(			self   ,      snake_case_   ,      snake_case_ = False   ,      snake_case_ = None   ,      snake_case_ = False   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      snake_case_ = None   ,      **snake_case_   ,      ):







										'''simple docstring'''


										if sampling_rate is not None:
																	if sampling_rate != self.sampling_rate:
																								raise ValueError(
																								    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
																								    F''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
																								    F''' {self.sampling_rate} and not {sampling_rate}.'''							)
										else:
																	logger.warning(
																	    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
																	    """Failing to do so can result in silent errors that might be hard to debug."""							)

										A__       :     Optional[int]								=			isinstance(snake_case_   ,      np.ndarray							) and len(raw_speech.shape							) > 1
										if is_batched_numpy and len(raw_speech.shape							) > 2:
																	raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''							)
										A__       :     Any								=			is_batched_numpy or (
										    isinstance(snake_case_   ,      (list, tuple)							) and (isinstance(raw_speech[0]   ,      (np.ndarray, tuple, list)							))
										)

										if is_batched:
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for speech in raw_speech]
										elif not is_batched and not isinstance(snake_case_   ,      np.ndarray							):
																	A__       :     Dict								=			np.asarray(snake_case_   ,      dtype=np.floataa							)
										elif isinstance(snake_case_   ,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
																	A__       :     Optional[int]								=			raw_speech.astype(np.floataa							)

										# always return batch
										if not is_batched:
																	A__       :     Union[str, Any]								=			[raw_speech]

										# extract fbank features
										A__       :     Optional[int]								=			[self._extract_mfsc_features(snake_case_							) for one_waveform in raw_speech]

										# convert into correct format for padding
										A__       :     str								=			BatchFeature({"""input_features""": features}							)

										A__       :     Any								=			self.pad(
										    snake_case_   ,      padding=snake_case_   ,      max_length=snake_case_   ,      truncation=snake_case_   ,      pad_to_multiple_of=snake_case_   ,      return_attention_mask=snake_case_   ,      **snake_case_   ,      )
										# make sure list is in array format
										A__       :     Any								=			padded_inputs.get("""input_features"""							)
										if isinstance(input_features[0]   ,      snake_case_							):
																	A__       :     Union[str, Any]								=			[np.asarray(snake_case_   ,      dtype=np.floataa							) for feature in input_features]

										A__       :     Optional[int]								=			padded_inputs.get("""attention_mask"""							)
										if attention_mask is not None:
																	A__       :     List[str]								=			[np.asarray(snake_case_   ,      dtype=np.intaa							) for array in attention_mask]

										if self.normalize_means or self.normalize_vars:
																	A__       :     Union[str, Any]								=			(
																	    np.array(snake_case_   ,      dtype=np.intaa							)
																	    if self._get_padding_strategies(snake_case_   ,      max_length=snake_case_							) is not PaddingStrategy.DO_NOT_PAD
																	    and padding
																	    else None
																	)
																	A__       :     Optional[Any]								=			self.normalize(
																	    padded_inputs["""input_features"""]   ,      attention_mask=snake_case_							)

										if return_tensors is not None:
																	A__       :     Tuple								=			padded_inputs.convert_to_tensors(snake_case_							)

										return padded_inputs






"""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)





