





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


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 lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Optional[int]:
   __snake_case							       =   ['''layers''', '''blocks''']
   for k in ignore_keys:
      state_dict.pop(snake_case_       ,      snake_case_       )


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 lowerCamelCase__    (							snake_case_	:  Optional[Any]       )      ->				Optional[Any]:
   __snake_case							       =   list(s_dict.keys()       )
   for key in keys:
      __snake_case							       =   key
      for k, v in WHISPER_MAPPING.items():
         if k in key:
            __snake_case							       =   new_key.replace(snake_case_       ,      snake_case_       )

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

      __snake_case							       =   s_dict.pop(snake_case_       )
   return s_dict






def lowerCamelCase__    (							snake_case_	:  str       )      ->				int:
   __snake_case       ,	__snake_case							       =   emb.weight.shape
   __snake_case							       =   nn.Linear(snake_case_       ,      snake_case_       ,      bias=snake_case_       )
   __snake_case							       =   emb.weight.data
   return lin_layer






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  str       )      ->				bytes:
   os.makedirs(snake_case_       ,      exist_ok=snake_case_       )
   __snake_case							       =   os.path.basename(snake_case_       )

   __snake_case							       =   url.split('''/'''       )[-2]
   __snake_case							       =   os.path.join(snake_case_       ,      snake_case_       )

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

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

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

   __snake_case							       =   open(snake_case_       ,      '''rb'''       ).read()
   if hashlib.shaaaa(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 lowerCamelCase__    (							snake_case_	:  Tuple       ,      snake_case_	:  Tuple       )      ->				List[Any]:
   if ".pt" not in checkpoint_path:
      __snake_case							       =   _download(_MODELS[checkpoint_path]       )
   else:
      __snake_case							       =   torch.load(snake_case_       ,      map_location='''cpu'''       )
   __snake_case							       =   original_checkpoint['''dims''']
   __snake_case							       =   original_checkpoint['''model_state_dict''']
   __snake_case							       =   state_dict['''decoder.token_embedding.weight''']
   remove_ignore_keys_(snake_case_       )
   rename_keys(snake_case_       )
   __snake_case							       =   True
   __snake_case							       =   state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

   __snake_case							       =   WhisperConfig(
       vocab_size=dimensions['''n_vocab''']       ,      encoder_ffn_dim=snake_case_       ,      decoder_ffn_dim=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''']       ,      )

   __snake_case							       =   WhisperForConditionalGeneration(snake_case_       )
   __snake_case       ,	__snake_case							       =   model.model.load_state_dict(snake_case_       ,      strict=snake_case_       )
   if len(snake_case_       ) > 0 and not set(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:
      __snake_case							       =   make_linear_from_emb(model.model.decoder.embed_tokens       )
   else:
      __snake_case							       =   proj_out_weights

   model.save_pretrained(snake_case_       )


if __name__ == "__main__":
    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.')
    snake_case_				=			parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
	import bsa
	from bsa import BeautifulSoup


UpperCAmelCase_ :						Any      =      logging.get_logger(__name__)

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Optional[int] ,  **__lowerCamelCase				:					Optional[int]	):
		requires_backends(self ,  ["""bs4"""]	)
		super().__init__(**__lowerCamelCase	)





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Any	):
		UpperCamelCase     :Optional[int]          =	[]
		UpperCamelCase     :List[str]          =	[]
		UpperCamelCase     :Union[str, Any]          =	element if element.name else element.parent
		for parent in child.parents:  # type: bs4.element.Tag
			UpperCamelCase     :Optional[Any]          =	parent.find_all(child.name ,  recursive=__lowerCamelCase	)
			xpath_tags.append(child.name	)
			xpath_subscripts.append(
			    0 if 1 == len(__lowerCamelCase	) else next(i for i, s in enumerate(__lowerCamelCase ,  1	) if s is child	)	)
			UpperCamelCase     :Any          =	parent
		xpath_tags.reverse()
		xpath_subscripts.reverse()
		return xpath_tags, xpath_subscripts





	def 						_A (		self				:					Any ,  __lowerCamelCase				:					Tuple	):
		UpperCamelCase     :Any          =	BeautifulSoup(__lowerCamelCase ,  """html.parser"""	)

		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :Tuple          =	[]
		UpperCamelCase     :Tuple          =	[]

		for element in html_code.descendants:
			if type(__lowerCamelCase	) == bsa.element.NavigableString:
				if type(element.parent	) != bsa.element.Tag:
					continue

				UpperCamelCase     :Any          =	html.unescape(__lowerCamelCase	).strip()
				if not text_in_this_tag:
					continue

				all_doc_strings.append(__lowerCamelCase	)

				UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	self.xpath_soup(__lowerCamelCase	)
				stringaxtag_seq.append(__lowerCamelCase	)
				stringaxsubs_seq.append(__lowerCamelCase	)

		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xtags does not correspond"""	)
		if len(__lowerCamelCase	) != len(__lowerCamelCase	):
			raise ValueError("""Number of doc strings and xsubs does not correspond"""	)

		return all_doc_strings, stringaxtag_seq, stringaxsubs_seq





	def 						_A (		self				:					int ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					List[str]	):
		UpperCamelCase     :Tuple          =	""""""
		for tagname, subs in zip(__lowerCamelCase ,  __lowerCamelCase	):
			xpath += F"""/{tagname}"""
			if subs != 0:
				xpath += F"""[{subs}]"""
		return xpath





	def __call__(		self				:					Any ,  __lowerCamelCase				:					Dict	):
		UpperCamelCase     :Any          =	False

		# Check that strings has a valid type
		if isinstance(__lowerCamelCase ,  __lowerCamelCase	):
			UpperCamelCase     :List[Any]          =	True
		elif isinstance(__lowerCamelCase ,  (list, tuple)	):
			if len(__lowerCamelCase	) == 0 or isinstance(html_strings[0] ,  __lowerCamelCase	):
				UpperCamelCase     :Any          =	True

		if not valid_strings:
			raise ValueError(
			    """HTML strings must of type `str`, `List[str]` (batch of examples), """
			    F"""but is of type {type(__lowerCamelCase	)}."""	)

		UpperCamelCase     :str          =	bool(isinstance(__lowerCamelCase ,  (list, tuple)	) and (isinstance(html_strings[0] ,  __lowerCamelCase	))	)

		if not is_batched:
			UpperCamelCase     :Any          =	[html_strings]

		# Get nodes + xpaths
		UpperCamelCase     :Union[str, Any]          =	[]
		UpperCamelCase     :str          =	[]
		for html_string in html_strings:
			UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :int          =	self.get_three_from_single(__lowerCamelCase	)
			nodes.append(__lowerCamelCase	)
			UpperCamelCase     :int          =	[]
			for node, tag_list, sub_list in zip(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	):
				UpperCamelCase     :str          =	self.construct_xpath(__lowerCamelCase ,  __lowerCamelCase	)
				xpath_strings.append(__lowerCamelCase	)
			xpaths.append(__lowerCamelCase	)

		# return as Dict
		UpperCamelCase     :Optional[int]          =	{"""nodes""": nodes, """xpaths""": xpaths}
		UpperCamelCase     :Any          =	BatchFeature(data=__lowerCamelCase ,  tensor_type=__lowerCamelCase	)

		return encoded_inputs
