





"""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


SCREAMING_SNAKE_CASE__   =  {
    '''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__				(    SCREAMING_SNAKE_CASE	:		Optional[int]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


SCREAMING_SNAKE_CASE__   =  {
    '''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__				(    SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''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(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

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

            lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
      return s_dict


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]     ):







      '''simple docstring'''




      os.makedirs(SCREAMING_SNAKE_CASE				,		exist_ok=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			os.path.basename(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			url.split("""/"""     )[-2]
      lowerCAmelCase    		=			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     ):
            lowerCAmelCase    		=			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=10_24     ) as loop:
                  while True:
                        lowerCAmelCase    		=			source.read(81_92     )
                        if not buffer:
                              break

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

      lowerCAmelCase    		=			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      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




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

      lowerCAmelCase    		=			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"""]				,		)

      lowerCAmelCase    		=			WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			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:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			proj_out_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


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

       convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)













import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Any			=   False, False, False

@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		None
	__UpperCamelCase   =		True
	__UpperCamelCase   =		True
	__UpperCamelCase   =		None
	# Automatically constructed
	__UpperCamelCase   =		"dict"
	__UpperCamelCase   =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}						)
	__UpperCamelCase   =		field(default='''Audio'''     ,     init=UpperCAmelCase_     ,     repr=UpperCAmelCase_						)




	def __call__(						self							:     Optional[Any]   )       -> List[str]:

			'''simple docstring'''




			return self.pa_type




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[str, bytes, dict]   )       -> dict:

			'''simple docstring'''




			try:
					import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
			except ImportError as err:
					raise ImportError('To support encoding audio data, please install \'soundfile\'.'   ) from err
			if isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": None, "path": value}
			elif isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": value, "path": None}
			elif "array" in value:
					# convert the audio array to wav bytes
					__lowercase											=     BytesIO()
					sf.write(__lowerCamelCase					,      value['array']					,      value['sampling_rate']					,      format='wav'   )
					return {"bytes": buffer.getvalue(), "path": None}
			elif value.get('path'   ) is not None and os.path.isfile(value['path']   ):
					# we set "bytes": None to not duplicate the data if they're already available locally
					if value["path"].endswith('pcm'   ):
							# "PCM" only has raw audio bytes
							if value.get('sampling_rate'   ) is None:
									# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
									raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'   )
							if value.get('bytes'   ):
									# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
									__lowercase											=     np.frombuffer(value['bytes']					,      dtype=np.intaa   ).astype(np.floataa   ) / 32_767
							else:
									__lowercase											=     np.memmap(value['path']					,      dtype='h'					,      mode='r'   ).astype(np.floataa   ) / 32_767

							__lowercase											=     BytesIO(bytes()   )
							sf.write(__lowerCamelCase					,      __lowerCamelCase					,      value['sampling_rate']					,      format='wav'   )
							return {"bytes": buffer.getvalue(), "path": None}
					else:
							return {"bytes": None, "path": value.get('path'   )}
			elif value.get('bytes'   ) is not None or value.get('path'   ) is not None:
					# store the audio bytes, and path is used to infer the audio format using the file extension
					return {"bytes": value.get('bytes'   ), "path": value.get('path'   )}
			else:
					raise ValueError(
					    F"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."   )




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     dict					,      __lowerCamelCase							:     Optional[Dict[str, Union[str, bool, None]]] = None   )       -> dict:

			'''simple docstring'''




			if not self.decode:
					raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'   )

			__lowercase						,      __lowercase											=     (value['path'], BytesIO(value['bytes']   )) if value['bytes'] is not None else (value['path'], None)
			if path is None and file is None:
					raise ValueError(F"An audio sample should have one of 'path' or 'bytes' but both are None in {value}."   )

			try:
					import librosa
					import soundfile as sf
			except ImportError as err:
					raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'   ) from err

			__lowercase											=     xsplitext(__lowerCamelCase   )[1][1:].lower() if path is not None else None
			if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
					raise RuntimeError(
					    'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )
			elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
					raise RuntimeError(
					    'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )

			if file is None:
					__lowercase											=     token_per_repo_id or {}
					__lowercase											=     path.split('::'   )[-1]
					try:
							__lowercase											=     string_to_dict(__lowerCamelCase					,      config.HUB_DATASETS_URL   )['repo_id']
							__lowercase											=     token_per_repo_id[repo_id]
					except (ValueError, KeyError):
							__lowercase											=     None

					with xopen(__lowerCamelCase					,      'rb'					,      use_auth_token=__lowerCamelCase   ) as f:
							__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			else:
					__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			__lowercase											=     array.T
			if self.mono:
					__lowercase											=     librosa.to_mono(__lowerCamelCase   )
			if self.sampling_rate and self.sampling_rate != sampling_rate:
					__lowercase											=     librosa.resample(__lowerCamelCase					,      orig_sr=__lowerCamelCase					,      target_sr=self.sampling_rate   )
					__lowercase											=     self.sampling_rate

			return {"path": path, "array": array, "sampling_rate": sampling_rate}




	def    UpperCAmelCase			(						self							:     int   )       -> Union["FeatureType", Dict[str, "FeatureType"]]:

			'''simple docstring'''




			from .features import Value

			if self.decode:
					raise ValueError('Cannot flatten a decoded Audio feature.'   )
			return {
			    "bytes": Value('binary'   ),
			    "path": Value('string'   ),
			}




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[pa.StringArray, pa.StructArray]   )       -> pa.StructArray:

			'''simple docstring'''




			if pa.types.is_string(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, storage]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_binary(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([storage, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_struct(storage.type   ) and storage.type.get_all_field_indices('array'   ):
					__lowercase											=     pa.array([Audio().encode_example(__lowerCamelCase   ) if x is not None else None for x in storage.to_pylist()]   )
			elif pa.types.is_struct(storage.type   ):
					if storage.type.get_field_index('bytes'   ) >= 0:
							__lowercase											=     storage.field('bytes'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					if storage.type.get_field_index('path'   ) >= 0:
							__lowercase											=     storage.field('path'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )






	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     pa.StructArray   )       -> pa.StructArray:

			'''simple docstring'''




			@no_op_if_value_is_null
			def path_to_bytes(__lowerCamelCase							:     Any   ):
					with xopen(__lowerCamelCase					,      'rb'   ) as f:
							__lowercase											=     f.read()
					return bytes_

			__lowercase											=     pa.array(
			    [
			        (path_to_bytes(x['path']   ) if x['bytes'] is None else x['bytes']) if x is not None else None
			        for x in storage.to_pylist()
			    ]					,      type=pa.binary()					,      )
			__lowercase											=     pa.array(
			    [os.path.basename(__lowerCamelCase   ) if path is not None else None for path in storage.field('path'   ).to_pylist()]					,      type=pa.string()					,      )
			__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=bytes_array.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )




