



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=0  ):





							'''simple docstring'''




							if name is None:
														_lowerCAmelCase     :							Union[str, Any]    =    None
							else:
														_lowerCAmelCase     :							Any    =    "." * max(0				, spaces - 2  ) + "# {:" + str(50 - spaces  ) + "s}"
														_lowerCAmelCase     :							Tuple    =    fmt.format(_lowerCamelCase  )

							# Print and recurse (if needed).
							if isinstance(_lowerCamelCase				, _lowerCamelCase  ):
														if msg is not None:
																					print(_lowerCamelCase  )
														for k in val.keys():
																					recursive_print(_lowerCamelCase				, val[k]				, spaces + 2  )
							elif isinstance(_lowerCamelCase				, torch.Tensor  ):
														print(_lowerCamelCase				, ":"				, val.size()  )
							else:
														print(_lowerCamelCase				, ":"				, _lowerCamelCase  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    param.size()
							if checkpoint_version == 1.0:
														# version 1.0 stores [num_heads * hidden_size * num_splits, :]
														_lowerCAmelCase     :							Optional[int]    =    (num_heads, hidden_size, num_splits) + input_shape[1:]
														_lowerCAmelCase     :							Dict    =    param.view(*_lowerCamelCase  )
														_lowerCAmelCase     :							Optional[Any]    =    param.transpose(0				, 2  )
														_lowerCAmelCase     :							str    =    param.transpose(1				, 2  ).contiguous()
							elif checkpoint_version >= 2.0:
														# other versions store [num_heads * num_splits * hidden_size, :]
														_lowerCAmelCase     :							Any    =    (num_heads, num_splits, hidden_size) + input_shape[1:]
														_lowerCAmelCase     :							List[Any]    =    param.view(*_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    param.transpose(0				, 1  ).contiguous()
							_lowerCAmelCase     :							Optional[int]    =    param.view(*_lowerCamelCase  )
							return param






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    {}

							# old versions did not store training args
							_lowerCAmelCase     :							Optional[Any]    =    input_state_dict.get("args"				, _lowerCamelCase  )
							if ds_args is not None:
														# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
														# from pprint import pprint
														# pprint(vars(ds_args))

														_lowerCAmelCase     :							List[Any]    =    ds_args.padded_vocab_size
														_lowerCAmelCase     :							Dict    =    ds_args.max_position_embeddings
														_lowerCAmelCase     :							Any    =    ds_args.hidden_size
														_lowerCAmelCase     :							List[Any]    =    ds_args.num_layers
														_lowerCAmelCase     :							Any    =    ds_args.num_attention_heads
														_lowerCAmelCase     :							str    =    ds_args.ffn_hidden_size
														# pprint(config)

							# The number of heads.
							_lowerCAmelCase     :							int    =    config.n_head
							# The hidden_size per head.
							_lowerCAmelCase     :							List[Any]    =    config.n_embd // config.n_head
							# Megatron-LM checkpoint version
							if "checkpoint_version" in input_state_dict.keys():
														_lowerCAmelCase     :							Tuple    =    input_state_dict["checkpoint_version"]
							else:
														_lowerCAmelCase     :							List[Any]    =    0.0

							# The model.
							_lowerCAmelCase     :							Tuple    =    input_state_dict["model"]
							# The language model.
							_lowerCAmelCase     :							List[Any]    =    model["language_model"]
							# The embeddings.
							_lowerCAmelCase     :							Any    =    lm["embedding"]

							# The word embeddings.
							_lowerCAmelCase     :							Union[str, Any]    =    embeddings["word_embeddings"]["weight"]
							# Truncate the embedding table to vocab_size rows.
							_lowerCAmelCase     :							Optional[int]    =    word_embeddings[: config.vocab_size, :]
							_lowerCAmelCase     :							int    =    word_embeddings

							# The position embeddings.
							_lowerCAmelCase     :							Any    =    embeddings["position_embeddings"]["weight"]
							# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
							_lowerCAmelCase     :							List[str]    =    pos_embeddings.size(0  )
							if n_positions != config.n_positions:
														raise ValueError(
														    F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"  )
							# Store the position embeddings.
							_lowerCAmelCase     :							int    =    pos_embeddings

							# The transformer.
							_lowerCAmelCase     :							int    =    lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

							# The regex to extract layer names.
							_lowerCAmelCase     :							Optional[int]    =    re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"  )

							# The simple map of names for "automated" rules.
							_lowerCAmelCase     :							int    =    {
							    "attention.dense": ".attn.c_proj.",
							    "self_attention.dense": ".attn.c_proj.",
							    "mlp.dense_h_to_4h": ".mlp.c_fc.",
							    "mlp.dense_4h_to_h": ".mlp.c_proj.",
							}

							# Extract the layers.
							for key, val in transformer.items():
														# Match the name.
														_lowerCAmelCase     :							Optional[Any]    =    layer_re.match(_lowerCamelCase  )

														# Stop if that's not a layer
														if m is None:
																					break

														# The index of the layer.
														_lowerCAmelCase     :							Optional[Any]    =    int(m.group(1  )  )
														# The name of the operation.
														_lowerCAmelCase     :							List[str]    =    m.group(2  )
														# Is it a weight or a bias?
														_lowerCAmelCase     :							List[Any]    =    m.group(3  )

														# The name of the layer.
														_lowerCAmelCase     :							str    =    F"transformer.h.{layer_idx}"

														# For layernorm(s), simply store the layer norm.
														if op_name.endswith("layernorm"  ):
																					_lowerCAmelCase     :							Optional[Any]    =    "ln_1" if op_name.startswith("input"  ) else "ln_2"
																					_lowerCAmelCase     :							List[Any]    =    val

														# Transpose the QKV matrix.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "weight":
																					# Insert a tensor of 1x1xDxD bias.
																					_lowerCAmelCase     :							Any    =    torch.tril(torch.ones((n_positions, n_positions)				, dtype=torch.floataa  )  ).view(
																					    1				, 1				, _lowerCamelCase				, _lowerCamelCase  )
																					_lowerCAmelCase     :							Dict    =    causal_mask

																					# Insert a "dummy" tensor for masked_bias.
																					_lowerCAmelCase     :							Tuple    =    torch.tensor(-1e4				, dtype=torch.floataa  )
																					_lowerCAmelCase     :							Tuple    =    masked_bias

																					_lowerCAmelCase     :							List[Any]    =    fix_query_key_value_ordering(_lowerCamelCase				, _lowerCamelCase				, 3				, _lowerCamelCase				, _lowerCamelCase  )
																					# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
																					_lowerCAmelCase     :							Dict    =    out_val.transpose(0				, 1  ).contiguous()
																					# Store.
																					_lowerCAmelCase     :							List[Any]    =    out_val

														# Transpose the bias.
														elif (
														    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
														) and weight_or_bias == "bias":
																					_lowerCAmelCase     :							Optional[int]    =    fix_query_key_value_ordering(_lowerCamelCase				, _lowerCamelCase				, 3				, _lowerCamelCase				, _lowerCamelCase  )
																					# Store. No change of shape.
																					_lowerCAmelCase     :							Tuple    =    out_val

														# Transpose the weights.
														elif weight_or_bias == "weight":
																					_lowerCAmelCase     :							Any    =    megatron_to_transformers[op_name]
																					_lowerCAmelCase     :							str    =    val.transpose(0				, 1  )

														# Copy the bias.
														elif weight_or_bias == "bias":
																					_lowerCAmelCase     :							Tuple    =    megatron_to_transformers[op_name]
																					_lowerCAmelCase     :							Dict    =    val

    # DEBUG.
							assert config.n_layer == layer_idx + 1

							# The final layernorm.
							_lowerCAmelCase     :							Dict    =    transformer["final_layernorm.weight"]
							_lowerCAmelCase     :							List[str]    =    transformer["final_layernorm.bias"]

							# For LM head, transformers' wants the matrix to weight embeddings.
							_lowerCAmelCase     :							Union[str, Any]    =    word_embeddings

							# It should be done!
							return output_state_dict






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    argparse.ArgumentParser()
							parser.add_argument("--print-checkpoint-structure"				, action="store_true"  )
							parser.add_argument(
							    "path_to_checkpoint"				, type=_lowerCamelCase				, help="Path to the checkpoint file (.zip archive or direct .pt file)"				, )
							parser.add_argument(
							    "--config_file"				, default=""				, type=_lowerCamelCase				, help="An optional config json file describing the pre-trained model."				, )
							_lowerCAmelCase     :							Union[str, Any]    =    parser.parse_args()

							# Extract the basename.
							_lowerCAmelCase     :							List[Any]    =    os.path.dirname(args.path_to_checkpoint  )

							# Load the model.
							# the .zip is very optional, let's keep it for backward compatibility
							print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}"  )
							if args.path_to_checkpoint.endswith(".zip"  ):
														with zipfile.ZipFile(args.path_to_checkpoint				, "r"  ) as checkpoint:
																					with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"  ) as pytorch_dict:
																												_lowerCAmelCase     :							int    =    torch.load(_lowerCamelCase				, map_location="cpu"  )
							else:
														_lowerCAmelCase     :							List[Any]    =    torch.load(args.path_to_checkpoint				, map_location="cpu"  )

							_lowerCAmelCase     :							Dict    =    input_state_dict.get("args"				, _lowerCamelCase  )

							# Read the config, or default to the model released by NVIDIA.
							if args.config_file == "":
														if ds_args is not None:
																					if ds_args.bias_gelu_fusion:
																												_lowerCAmelCase     :							Union[str, Any]    =    "gelu_fast"
																					elif ds_args.openai_gelu:
																												_lowerCAmelCase     :							int    =    "gelu_new"
																					else:
																												_lowerCAmelCase     :							Dict    =    "gelu"
														else:
																					# in the very early days this used to be "gelu_new"
																					_lowerCAmelCase     :							Dict    =    "gelu_new"

														# Spell out all parameters in case the defaults change.
														_lowerCAmelCase     :							Union[str, Any]    =    GPTaConfig(
														    vocab_size=50_257				, n_positions=1_024				, n_embd=1_024				, n_layer=24				, n_head=16				, n_inner=4_096				, activation_function=_lowerCamelCase				, resid_pdrop=0.1				, embd_pdrop=0.1				, attn_pdrop=0.1				, layer_norm_epsilon=1e-5				, initializer_range=0.02				, summary_type="cls_index"				, summary_use_proj=_lowerCamelCase				, summary_activation=_lowerCamelCase				, summary_proj_to_labels=_lowerCamelCase				, summary_first_dropout=0.1				, scale_attn_weights=_lowerCamelCase				, use_cache=_lowerCamelCase				, bos_token_id=50_256				, eos_token_id=50_256				, )
							else:
														_lowerCAmelCase     :							str    =    GPTaConfig.from_json_file(args.config_file  )

							_lowerCAmelCase     :							Dict    =    ["GPT2LMHeadModel"]

							# Convert.
							print("Converting"  )
							_lowerCAmelCase     :							Tuple    =    convert_megatron_checkpoint(_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase  )

							# Print the structure of converted state dict.
							if args.print_checkpoint_structure:
														recursive_print(_lowerCamelCase				, _lowerCamelCase  )

							# Add tokenizer class info to config
							# see https://github.com/huggingface/transformers/issues/13906)
							if ds_args is not None:
														_lowerCAmelCase     :							Optional[int]    =    ds_args.tokenizer_type
														if tokenizer_type == "GPT2BPETokenizer":
																					_lowerCAmelCase     :							Optional[int]    =    "gpt2"
														elif tokenizer_type == "PretrainedFromHF":
																					_lowerCAmelCase     :							Union[str, Any]    =    ds_args.tokenizer_name_or_path
														else:
																					raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}"  )
							else:
														_lowerCAmelCase     :							str    =    "gpt2"

							_lowerCAmelCase     :							Union[str, Any]    =    AutoTokenizer.from_pretrained(_lowerCamelCase  )
							_lowerCAmelCase     :							Dict    =    type(_lowerCamelCase  ).__name__
							_lowerCAmelCase     :							List[str]    =    tokenizer_class

							# Store the config to file.
							print("Saving config"  )
							config.save_pretrained(_lowerCamelCase  )

							# Save tokenizer based on args
							print(F"Adding {tokenizer_class} tokenizer files"  )
							tokenizer.save_pretrained(_lowerCamelCase  )

							# Store the state_dict to file.
							_lowerCAmelCase     :							List[str]    =    os.path.join(_lowerCamelCase				, "pytorch_model.bin"  )
							print(F"Saving checkpoint to \"{output_checkpoint_file}\""  )
							torch.save(_lowerCamelCase				, _lowerCamelCase  )


####################################################################################################

if __name__ == "__main__":
						main()

####################################################################################################







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


lowerCAmelCase_    =				{
    '''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_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  ['''layers''', '''blocks''']
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase ,				_UpperCamelCase )


lowerCAmelCase_    =				{
    '''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_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_						:    str				=  list(s_dict.keys() )
				for key in keys:
								snake_case_						:    Optional[int]				=  key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																snake_case_						:    List[str]				=  new_key.replace(_UpperCamelCase ,				_UpperCamelCase )

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

								snake_case_						:    Tuple				=  s_dict.pop(_UpperCamelCase )
				return s_dict






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    Dict				=  emb.weight.shape
				snake_case_						:    Tuple				=  nn.Linear(_UpperCamelCase ,				_UpperCamelCase ,				bias=_UpperCamelCase )
				snake_case_						:    Any				=  emb.weight.data
				return lin_layer






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						bytes:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase )
				snake_case_						:    List[Any]				=  os.path.basename(_UpperCamelCase )

				snake_case_						:    Any				=  url.split('''/''' )[-2]
				snake_case_						:    str				=  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 ):
								snake_case_						:    Union[str, Any]				=  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=1_024 ) as loop:
												while True:
																snake_case_						:    Dict				=  source.read(8_192 )
																if not buffer:
																				break

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

				snake_case_						:    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      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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

				snake_case_						:    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'''] ,				)

				snake_case_						:    Union[str, Any]				=  WhisperForConditionalGeneration(_UpperCamelCase )
				snake_case_			,							snake_case_						:    List[Any]				=  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:
								snake_case_						:    List[str]				=  make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								snake_case_						:    Any				=  proj_out_weights

				model.save_pretrained(_UpperCamelCase )


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

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
