





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 			=  {
    '''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      (			a_						:Any)		->		List[str]:
				__a :						List[str]			       =  ["""layers""", """blocks"""]
				for k in ignore_keys:
								state_dict.pop(a_   ,       a_)


A 			=  {
    '''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      (			a_						:Union[str, Any])		->		Optional[Any]:
				__a :						Union[str, Any]			       =  list(s_dict.keys())
				for key in keys:
								__a :						Dict			       =  key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																__a :						Tuple			       =  new_key.replace(a_   ,       a_)

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

								__a :						int			       =  s_dict.pop(a_)
				return s_dict

def     __A      (			a_						:List[Any])		->		Optional[Any]:
				__a :						int			       =  emb.weight.shape
				__a :						Optional[int]			       =  nn.Linear(a_   ,       a_   ,       bias=a_)
				__a :						List[str]			       =  emb.weight.data
				return lin_layer

def     __A      (			a_						:str   ,       a_						:str)		->		bytes:
				os.makedirs(a_   ,       exist_ok=a_)
				__a :						Any			       =  os.path.basename(a_)

				__a :						int			       =  url.split('''/''')[-2]
				__a :						Optional[int]			       =  os.path.join(a_   ,       a_)

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

				if os.path.isfile(a_):
								__a :						Dict			       =  open(a_   ,       '''rb''').read()
								if hashlib.shaaaa(a_).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(a_) as source, open(a_   ,       '''wb''') as output:
								with tqdm(
								    total=int(source.info().get('''Content-Length'''))   ,       ncols=80   ,       unit='''iB'''   ,       unit_scale=a_   ,       unit_divisor=10_24) as loop:
												while True:
																__a :						Tuple			       =  source.read(81_92)
																if not buffer:
																				break

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

				__a :						Any			       =  open(a_   ,       '''rb''').read()
				if hashlib.shaaaa(a_).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      (			a_						:Dict   ,       a_						:Any)		->		Optional[Any]:







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

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

				model.save_pretrained(a_)


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

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""






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


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			**_UpperCAmelCase     ):
									requires_backends(self	,			['''bs4''']     )
									super().__init__(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Tuple			       =  []
									__a :						Dict			       =  []
									__a :						int			       =  element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
													__a :						Dict			       =  parent.find_all(child.name	,			recursive=_UpperCAmelCase     )
													xpath_tags.append(child.name     )
													xpath_subscripts.append(
													    0 if 1 == len(_UpperCAmelCase     ) else next(i for i, s in enumerate(_UpperCAmelCase	,			1     ) if s is child     )     )
													__a :						Any			       =  parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Dict			       =  BeautifulSoup(_UpperCAmelCase	,			'''html.parser'''     )

									__a :						Any			       =  []
									__a :						Dict			       =  []
									__a :						Optional[int]			       =  []

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

																	__a :						Dict			       =  html.unescape(_UpperCAmelCase     ).strip()
																	if not text_in_this_tag:
																					continue

																	all_doc_strings.append(_UpperCAmelCase     )

																	__a       ,						__a :						List[Any]			       =  self.xpath_soup(_UpperCAmelCase     )
																	stringaxtag_seq.append(_UpperCAmelCase     )
																	stringaxsubs_seq.append(_UpperCAmelCase     )

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

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  ''''''
									for tagname, subs in zip(_UpperCAmelCase	,			_UpperCAmelCase     ):
													xpath += f"""/{tagname}"""
													if subs != 0:
																	xpath += f"""[{subs}]"""
									return xpath







					def __call__(						self	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  False

									# Check that strings has a valid type
									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
													__a :						str			       =  True
									elif isinstance(_UpperCAmelCase	,			(list, tuple)     ):
													if len(_UpperCAmelCase     ) == 0 or isinstance(html_strings[0]	,			_UpperCAmelCase     ):
																	__a :						Optional[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(_UpperCAmelCase     )}."""     )

									__a :						List[str]			       =  bool(isinstance(_UpperCAmelCase	,			(list, tuple)     ) and (isinstance(html_strings[0]	,			_UpperCAmelCase     ))     )

									if not is_batched:
													__a :						List[Any]			       =  [html_strings]

									# Get nodes + xpaths
									__a :						Tuple			       =  []
									__a :						int			       =  []
									for html_string in html_strings:
													__a       ,						__a       ,						__a :						Any			       =  self.get_three_from_single(_UpperCAmelCase     )
													nodes.append(_UpperCAmelCase     )
													__a :						List[Any]			       =  []
													for node, tag_list, sub_list in zip(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						Optional[int]			       =  self.construct_xpath(_UpperCAmelCase	,			_UpperCAmelCase     )
																	xpath_strings.append(_UpperCAmelCase     )
													xpaths.append(_UpperCAmelCase     )

									# return as Dict
									__a :						int			       =  {'''nodes''': nodes, '''xpaths''': xpaths}
									__a :						Any			       =  BatchFeature(data=_UpperCAmelCase	,			tensor_type=_UpperCAmelCase     )

									return encoded_inputs