from abc import ABC, abstractmethod
from typing import List, Optional






class      __lowercase					(    _A    ):

						def __init__(	self				:      Tuple   )							->		Union[str, Any]:


									'''simple docstring'''

									self.test()

						def        __a		(	self				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		0
									lowercase							=		False
									while not completed:
												if counter == 1:
															self.reset()
												lowercase							=		self.advance()
												if not self.does_advance(__lowerCamelCase   ):
															raise Exception(
															    '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''   )

												lowercase		,lowercase		,lowercase							=		self.update(__lowerCamelCase   )
												counter += 1

												if counter > 1_00_00:
															raise Exception('''update() does not fulfill the constraint.'''   )

									if self.remaining() != 0:
												raise Exception('''Custom Constraint is not defined correctly.'''   )

						@abstractmethod
						def        __a		(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )

						@abstractmethod
						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      int   )							->		List[Any]:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )

						@abstractmethod
						def        __a		(	self				:      Any							,							__lowerCamelCase				:      int   )							->		Optional[Any]:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )

						@abstractmethod
						def        __a		(	self				:      Tuple   )							->		int:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )

						@abstractmethod
						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )



						@abstractmethod
						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[Any]=False   )							->		Union[str, Any]:


									'''simple docstring'''

									raise NotImplementedError(
									    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'   )






class      __lowercase					(    _A    ):

						def __init__(	self				:      Any							,							__lowerCamelCase				:      List[int]   )							->		Tuple:


									'''simple docstring'''

									super(__lowerCamelCase							,							self   ).__init__()

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
												raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.'   )
									if any((not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) or token_id < 0) for token_id in token_ids   ):
												raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'   )

									lowercase							=		token_ids

									lowercase							=		len(self.token_ids   )
									lowercase							=		-1  # the index of the currently fulfilled step
									lowercase							=		False

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									if self.completed:
												return None
									return self.token_ids[self.fulfilled_idx + 1]

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		List[str]:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(__lowerCamelCase   )}'   )

									if self.completed:
												return False

									return token_id == self.token_ids[self.fulfilled_idx + 1]

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      int   )							->		Optional[int]:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(__lowerCamelCase   )}'   )

									lowercase							=		False
									lowercase							=		False
									lowercase							=		False

									if self.does_advance(__lowerCamelCase   ):
												self.fulfilled_idx += 1
												lowercase							=		True
												if self.fulfilled_idx == (self.seqlen - 1):
															lowercase							=		True
												lowercase							=		completed
									else:
												# failed to make progress.
												lowercase							=		True
												self.reset()
									return stepped, completed, reset

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


									'''simple docstring'''

									lowercase							=		False
									lowercase							=		0

						def        __a		(	self				:      str   )							->		List[str]:


									'''simple docstring'''

									return self.seqlen - (self.fulfilled_idx + 1)



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]=False   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		PhrasalConstraint(self.token_ids   )

									if stateful:
												lowercase							=		self.seqlen
												lowercase							=		self.fulfilled_idx
												lowercase							=		self.completed

									return new_constraint






class      __lowercase					:

						def __init__(	self				:      int							,							__lowerCamelCase				:      List[List[int]]							,							__lowerCamelCase				:      Optional[Any]=True   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		max([len(__lowerCamelCase   ) for one in nested_token_ids]   )

									lowercase							=		{}
									for token_ids in nested_token_ids:
												lowercase							=		root
												for tidx, token_id in enumerate(__lowerCamelCase   ):
															if token_id not in level:
																		lowercase							=		{}

															lowercase							=		level[token_id]

									if no_subsets and self.has_subsets(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(
												    '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
												    f' {nested_token_ids}.'   )

									lowercase							=		root

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Dict   )							->		int:


									'''simple docstring'''

									lowercase							=		self.trie

									for current_token in current_seq:
												lowercase							=		start[current_token]

									lowercase							=		list(start.keys()   )

									return next_tokens

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Optional[Any]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.next_tokens(__lowerCamelCase   )

									return len(__lowerCamelCase   ) == 0

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		list(root.values()   )
									if len(__lowerCamelCase   ) == 0:
												return 1
									else:
												return sum([self.count_leaves(__lowerCamelCase   ) for nn in next_nodes]   )



						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Tuple   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		self.count_leaves(__lowerCamelCase   )
									return len(__lowerCamelCase   ) != leaf_count






class      __lowercase					(    _A    ):

						def __init__(	self				:      Tuple							,							__lowerCamelCase				:      List[List[int]]   )							->		Union[str, Any]:


									'''simple docstring'''

									super(__lowerCamelCase							,							self   ).__init__()

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
												raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'   )
									if any(not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) for token_ids in nested_token_ids   ):
												raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'   )
									if any(
									    any((not isinstance(__lowerCamelCase							,							__lowerCamelCase   ) or token_id < 0) for token_id in token_ids   )
									    for token_ids in nested_token_ids   ):
												raise ValueError(
												    f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'   )

									lowercase							=		DisjunctiveTrie(__lowerCamelCase   )
									lowercase							=		nested_token_ids

									lowercase							=		self.trie.max_height
									lowercase							=		[]
									lowercase							=		False

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.trie.next_tokens(self.current_seq   )

									if len(__lowerCamelCase   ) == 0:
												return None
									else:
												return token_list

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      int   )							->		Any:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__lowerCamelCase   )}'   )

									lowercase							=		self.trie.next_tokens(self.current_seq   )

									return token_id in next_tokens

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      int   )							->		Tuple:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(__lowerCamelCase   )}'   )

									lowercase							=		False
									lowercase							=		False
									lowercase							=		False

									if self.does_advance(__lowerCamelCase   ):
												self.current_seq.append(__lowerCamelCase   )
												lowercase							=		True
									else:
												lowercase							=		True
												self.reset()

									lowercase							=		self.trie.reached_leaf(self.current_seq   )
									lowercase							=		completed

									return stepped, completed, reset

						def        __a		(	self				:      Dict   )							->		str:


									'''simple docstring'''

									lowercase							=		False
									lowercase							=		[]

						def        __a		(	self				:      List[str]   )							->		Dict:


									'''simple docstring'''

									if self.completed:
												# since this can be completed without reaching max height
												return 0
									else:
												return self.seqlen - len(self.current_seq   )



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Any=False   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		DisjunctiveConstraint(self.token_ids   )

									if stateful:
												lowercase							=		self.seqlen
												lowercase							=		self.current_seq
												lowercase							=		self.completed

									return new_constraint






class      __lowercase					:

						def __init__(	self				:      Union[str, Any]							,							__lowerCamelCase				:      List[Constraint]   )							->		Any:


									'''simple docstring'''

									lowercase							=		constraints

									# max # of steps required to fulfill a given constraint
									lowercase							=		max([c.seqlen for c in constraints]   )
									lowercase							=		len(__lowerCamelCase   )
									lowercase							=		False

									self.init_state()

						def        __a		(	self				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									lowercase							=		None
									lowercase							=		[constraint.copy(stateful=__lowerCamelCase   ) for constraint in self.constraints]

						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									lowercase							=		0
									if self.inprogress_constraint:
												# extra points for having a constraint mid-fulfilled
												add += self.max_seqlen - self.inprogress_constraint.remaining()

									return (len(self.complete_constraints   ) * self.max_seqlen) + add

						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		[]
									if self.inprogress_constraint is None:
												for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
															lowercase							=		constraint.advance()
															if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
																		token_list.append(__lowerCamelCase   )
															elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
																		token_list.extend(__lowerCamelCase   )
									else:
												lowercase							=		self.inprogress_constraint.advance()
												if isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
															token_list.append(__lowerCamelCase   )
												elif isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
															token_list.extend(__lowerCamelCase   )

									if len(__lowerCamelCase   ) == 0:
												return None
									else:
												return token_list

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Optional[List[int]]   )							->		Dict:


									'''simple docstring'''

									self.init_state()

									if token_ids is not None:
												for token in token_ids:
															# completes or steps **one** constraint
															lowercase		,lowercase							=		self.add(__lowerCamelCase   )

															# the entire list of constraints are fulfilled
															if self.completed:
																		break

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.'   )

									lowercase		,lowercase							=		False, False

									if self.completed:
												lowercase							=		True
												lowercase							=		False
												return complete, stepped

									if self.inprogress_constraint is not None:
												# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
												# job, simply update the state

												lowercase		,lowercase		,lowercase							=		self.inprogress_constraint.update(__lowerCamelCase   )
												if reset:
															# 1. If the next token breaks the progress, then we must restart.
															#     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

															#     But that doesn't mean we self.init_state(), since we only reset the state for this particular
															#     constraint, not the full list of constraints.

															self.pending_constraints.append(self.inprogress_constraint.copy(stateful=__lowerCamelCase   )   )
															lowercase							=		None

												if complete:
															# 2. If the next token completes the constraint, move it to completed list, set
															#     inprogress to None. If there are no pending constraints either, then this full list of constraints
															#     is complete.

															self.complete_constraints.append(self.inprogress_constraint   )
															lowercase							=		None

															if len(self.pending_constraints   ) == 0:
																		# we're done!
																		lowercase							=		True

									else:
												# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
												# of constraints?

												for cidx, pending_constraint in enumerate(self.pending_constraints   ):
															if pending_constraint.does_advance(__lowerCamelCase   ):
																		lowercase		,lowercase		,lowercase							=		pending_constraint.update(__lowerCamelCase   )

																		if not stepped:
																					raise Exception(
																					    '''`constraint.update(token_id)` is not yielding incremental progress, '''
																					    '''even though `constraint.does_advance(token_id)` is true.'''   )

																		if complete:
																					self.complete_constraints.append(__lowerCamelCase   )
																					lowercase							=		None

																		if not complete and stepped:
																					lowercase							=		pending_constraint

																		if complete or stepped:
																					# If we made any progress at all, then it's at least not a "pending constraint".

																					lowercase							=		(
																					    self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
																					)

																					if len(self.pending_constraints   ) == 0 and self.inprogress_constraint is None:
																								# If there's no longer any pending after this and no inprogress either, then we must be
																								# complete.

																								lowercase							=		True

																					break  # prevent accidentally stepping through multiple constraints with just one token.

									return complete, stepped



						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      int=True   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		ConstraintListState(self.constraints   )  # we actually never though self.constraints objects
									# throughout this process. So it's at initialization state.

									if stateful:
												lowercase							=		[
												    constraint.copy(stateful=__lowerCamelCase   ) for constraint in self.complete_constraints
												]
												if self.inprogress_constraint is not None:
															lowercase							=		self.inprogress_constraint.copy(stateful=__lowerCamelCase   )
												lowercase							=		[constraint.copy() for constraint in self.pending_constraints]

									return new_state






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        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			lowercase							=		['''layers''', '''blocks''']
			for k in ignore_keys:
						state_dict.pop(UpperCAmelCase,		UpperCAmelCase       )


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        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""simple docstring"""







			lowercase							=		list(s_dict.keys()       )
			for key in keys:
						lowercase							=		key
						for k, v in WHISPER_MAPPING.items():
									if k in key:
												lowercase							=		new_key.replace(UpperCAmelCase,		UpperCAmelCase       )

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

						lowercase							=		s_dict.pop(UpperCAmelCase       )
			return s_dict






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase		,lowercase							=		emb.weight.shape
			lowercase							=		nn.Linear(UpperCAmelCase,		UpperCAmelCase,		bias=UpperCAmelCase       )
			lowercase							=		emb.weight.data
			return lin_layer






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       bytes:



			"""simple docstring"""







			os.makedirs(UpperCAmelCase,		exist_ok=UpperCAmelCase       )
			lowercase							=		os.path.basename(UpperCAmelCase       )

			lowercase							=		url.split('''/'''       )[-2]
			lowercase							=		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       ):
						lowercase							=		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:
												lowercase							=		source.read(8192       )
												if not buffer:
															break

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

			lowercase							=		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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







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

			lowercase							=		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'''],		)

			lowercase							=		WhisperForConditionalGeneration(UpperCAmelCase       )
			lowercase		,lowercase							=		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:
						lowercase							=		make_linear_from_emb(model.model.decoder.embed_tokens       )
			else:
						lowercase							=		proj_out_weights

			model.save_pretrained(UpperCAmelCase       )


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)





