



import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_lowercase     							=						object()

# For specifying empty leaf dict `{}`
_lowercase     							=						object()






def      _A				(UpperCamelCase		:      Optional[Any]      ,			UpperCamelCase		:      List[Any] )  ->Tuple:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		tuple((re.compile(x + """$""" ) for x in qs) )
			for i in range(len(UpperCamelCase ) - len(UpperCamelCase ) + 1 ):
						lowerCamelCase__ :      Union[str, Any]								=		[x.match(UpperCamelCase ) for x, y in zip(UpperCamelCase      ,			ks[i:] )]
						if matches and all(UpperCamelCase ):
									return True
			return False






def      _A				(UpperCamelCase		:      str )  ->List[Any]:



			'''simple docstring'''







			def replace(UpperCamelCase		:      Any      ,			UpperCamelCase		:      Union[str, Any] ):
						for rule, replacement in rules:
									if _match(UpperCamelCase      ,			UpperCamelCase ):
												return replacement
						return val

			return replace






def      _A				()  ->int:



			'''simple docstring'''







			return [
			    # embeddings
			    (("transformer", "wpe", "embedding"), P("""mp"""      ,			UpperCamelCase )),
			    (("transformer", "wte", "embedding"), P("""mp"""      ,			UpperCamelCase )),
			    # atention
			    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase      ,			"""mp""" )),
			    (("attention", "out_proj", "kernel"), P("""mp"""      ,			UpperCamelCase )),
			    (("attention", "out_proj", "bias"), None),
			    # mlp
			    (("mlp", "c_fc", "kernel"), P(UpperCamelCase      ,			"""mp""" )),
			    (("mlp", "c_fc", "bias"), P("""mp""" )),
			    (("mlp", "c_proj", "kernel"), P("""mp"""      ,			UpperCamelCase )),
			    (("mlp", "c_proj", "bias"), None),
			    # layer norms
			    ((r"ln_\d+", "bias"), None),
			    ((r"\d+", r"ln_\d+", "scale"), None),
			    (("ln_f", "bias"), None),
			    (("ln_f", "scale"), None),
			]






def      _A				(UpperCamelCase		:      str )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		_get_partition_rules()
			lowerCamelCase__ :      int								=		_replacement_rules(UpperCamelCase )
			lowerCamelCase__ :      str								=		{k: _unmatched for k in flatten_dict(UpperCamelCase )}
			lowerCamelCase__ :      Union[str, Any]								=		{k: replace(UpperCamelCase      ,			UpperCamelCase ) for k, v in initd.items()}
			assert _unmatched not in result.values(), "Incomplete partition spec."
			return freeze(unflatten_dict(UpperCamelCase ) )








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


_lowercase     							=						{
    '''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				(UpperCamelCase		:      Any )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		["""layers""", """blocks"""]
			for k in ignore_keys:
						state_dict.pop(UpperCamelCase      ,			UpperCamelCase )


_lowercase     							=						{
    '''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				(UpperCamelCase		:      Union[str, Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Union[str, Any]								=		list(s_dict.keys() )
			for key in keys:
						lowerCamelCase__ :      Dict								=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowerCamelCase__ :      Tuple								=		new_key.replace(UpperCamelCase      ,			UpperCamelCase )

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

						lowerCamelCase__ :      int								=		s_dict.pop(UpperCamelCase )
			return s_dict






def      _A				(UpperCamelCase		:      List[Any] )  ->Optional[Any]:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      int								=		emb.weight.shape
			lowerCamelCase__ :      Optional[int]								=		nn.Linear(UpperCamelCase      ,			UpperCamelCase      ,			bias=UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		emb.weight.data
			return lin_layer






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->bytes:



			'''simple docstring'''







			os.makedirs(UpperCamelCase      ,			exist_ok=UpperCamelCase )
			lowerCamelCase__ :      Any								=		os.path.basename(UpperCamelCase )

			lowerCamelCase__ :      int								=		url.split("""/""" )[-2]
			lowerCamelCase__ :      Optional[int]								=		os.path.join(UpperCamelCase      ,			UpperCamelCase )

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

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

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

			lowerCamelCase__ :      Any								=		open(UpperCamelCase      ,			"""rb""" ).read()
			if hashlib.shaaaa(UpperCamelCase ).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				(UpperCamelCase		:      Dict      ,			UpperCamelCase		:      Any )  ->Optional[Any]:



			'''simple docstring'''







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

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

			model.save_pretrained(UpperCamelCase )


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

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



