



"""simple docstring"""





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       :					List[Any]      					=					{
    '''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      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			["""layers""", """blocks"""]
						for k in ignore_keys:
												state_dict.pop(__UpperCamelCase					, __UpperCamelCase    )


__lowerCAmelCase       :					Dict      					=					{
    '''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      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[int]						=			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_   : Optional[Any]						=			new_key.replace(__UpperCamelCase					, __UpperCamelCase    )

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

												snake_case_   : Tuple						=			s_dict.pop(__UpperCamelCase    )
						return s_dict
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_			,		snake_case_   : str						=			emb.weight.shape
						snake_case_   : List[str]						=			nn.Linear(__UpperCamelCase					, __UpperCamelCase					, bias=__UpperCamelCase    )
						snake_case_   : Optional[Any]						=			emb.weight.data
						return lin_layer
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						os.makedirs(__UpperCamelCase					, exist_ok=__UpperCamelCase    )
						snake_case_   : Optional[int]						=			os.path.basename(__UpperCamelCase    )

						snake_case_   : List[str]						=			url.split("""/"""    )[-2]
						snake_case_   : Tuple						=			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_   : Optional[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=8_0					, unit="""iB"""					, unit_scale=__UpperCamelCase					, unit_divisor=1_0_2_4    ) as loop:
																		while True:
																								snake_case_   : Optional[int]						=			source.read(8_1_9_2    )
																								if not buffer:
																														break

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

						snake_case_   : List[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      :			List[Any]					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






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

						snake_case_   : Dict						=			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_   : Optional[Any]						=			WhisperForConditionalGeneration(__UpperCamelCase    )
						snake_case_			,		snake_case_   : Dict						=			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_   : Optional[int]						=			make_linear_from_emb(model.model.decoder.embed_tokens    )
						else:
												snake_case_   : Any						=			proj_out_weights

						model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					List[str]      					=					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       :					List[str]      					=					parser.parse_args()

					convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : Any						=			1  # To kept the Calculated Value
						# Since C(n, k) = C(n, n-k)
						if k > (n - k):
												snake_case_   : Optional[int]						=			n - k
						# Calculate C(n,k)
						for i in range(__UpperCamelCase    ):
												result *= n - i
												result //= i + 1
						return result
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return binomial_coefficient(2 * node_count					, __UpperCamelCase    ) // (node_count + 1)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						if n < 0:
												raise ValueError("""factorial() not defined for negative values"""    )
						snake_case_   : Optional[int]						=			1
						for i in range(1					, n + 1    ):
												result *= i
						return result
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						return catalan_number(__UpperCamelCase    ) * factorial(__UpperCamelCase    )


if __name__ == "__main__":
					__lowerCAmelCase       :					Optional[Any]      					=					int(input('''Enter the number of nodes: ''').strip() or 0)
					if node_count <= 0:
										raise ValueError('''We need some nodes to work with.''')
					print(
					    F'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
					    F'''binary trees and {catalan_number(node_count)} binary search trees.'''
					)

