






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


SCREAMING_SNAKE_CASE__      :			Optional[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      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__		:Optional[Any]        =      ['''layers''', '''blocks''']
				for k in ignore_keys:
								state_dict.pop(snake_case,  snake_case )


SCREAMING_SNAKE_CASE__      :			str							=		{
    """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      __lowercase					( snake_case ):







				"""simple docstring"""






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

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

								__magic_name__		:Dict        =      s_dict.pop(snake_case )
				return s_dict

def      __lowercase					( snake_case ):







				"""simple docstring"""






				__magic_name__     ,				__magic_name__		:Union[str, Any]        =      emb.weight.shape
				__magic_name__		:List[Any]        =      nn.Linear(snake_case,  snake_case,  bias=snake_case )
				__magic_name__		:List[Any]        =      emb.weight.data
				return lin_layer

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				os.makedirs(snake_case,  exist_ok=snake_case )
				__magic_name__		:str        =      os.path.basename(snake_case )

				__magic_name__		:Union[str, Any]        =      url.split('''/''' )[-2]
				__magic_name__		:Union[str, Any]        =      os.path.join(snake_case,  snake_case )

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

				if os.path.isfile(snake_case ):
								__magic_name__		:Dict        =      open(snake_case,  '''rb''' ).read()
								if hashlib.shaaaa(snake_case ).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(snake_case ) as source, open(snake_case,  '''wb''' ) as output:
								with tqdm(
								    total=int(source.info().get('''Content-Length''' ) ),  ncols=8_0,  unit='''iB''',  unit_scale=snake_case,  unit_divisor=1_0_2_4 ) as loop:
												while True:
																__magic_name__		:int        =      source.read(8_1_9_2 )
																if not buffer:
																				break

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

				__magic_name__		:Tuple        =      open(snake_case,  '''rb''' ).read()
				if hashlib.shaaaa(snake_case ).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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






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

				__magic_name__		:Tuple        =      WhisperConfig(
				    vocab_size=dimensions['''n_vocab'''],  encoder_ffn_dim=snake_case,  decoder_ffn_dim=snake_case,  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'''],  )

				__magic_name__		:Tuple        =      WhisperForConditionalGeneration(snake_case )
				__magic_name__     ,				__magic_name__		:int        =      model.model.load_state_dict(snake_case,  strict=snake_case )
				if len(snake_case ) > 0 and not set(snake_case ) <= {
				    "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:
								__magic_name__		:int        =      make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								__magic_name__		:Dict        =      proj_out_weights

				model.save_pretrained(snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		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.""")
					SCREAMING_SNAKE_CASE__      :			Dict							=		parser.parse_args()

					convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)














import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								self.assertEqual(x.component(0     )       ,					1     )
								self.assertEqual(x.component(2     )       ,					3     )
								__magic_name__		:Tuple        =      Vector()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 1]     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,0,0,0,0,1)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      Vector([1, 2, 3, 4]     )
								self.assertEqual(len(__lowerCAmelCase     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      Vector([1, 2]     )
								__magic_name__		:int        =      Vector([1, 2, 3, 4, 5]     )
								__magic_name__		:Any        =      Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[Any]        =      Vector([1, -1, 1, -1, 2, -3, 4, -5]     )
								self.assertAlmostEqual(x.euclidean_length()       ,					2.236       ,					3     )
								self.assertAlmostEqual(y.euclidean_length()       ,					7.416       ,					3     )
								self.assertEqual(z.euclidean_length()       ,					0     )
								self.assertAlmostEqual(w.euclidean_length()       ,					7.616       ,					3     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Vector([1, 2, 3]     )
								__magic_name__		:List[str]        =      Vector([1, 1, 1]     )
								self.assertEqual((x + y).component(0     )       ,					2     )
								self.assertEqual((x + y).component(1     )       ,					3     )
								self.assertEqual((x + y).component(2     )       ,					4     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 2, 3]     )
								__magic_name__		:Union[str, Any]        =      Vector([1, 1, 1]     )
								self.assertEqual((x - y).component(0     )       ,					0     )
								self.assertEqual((x - y).component(1     )       ,					1     )
								self.assertEqual((x - y).component(2     )       ,					2     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Vector([1, 2, 3]     )
								__magic_name__		:Optional[int]        =      Vector([2, -1, 4]     )  # for test of dot product
								__magic_name__		:List[Any]        =      Vector([1, -2, -1]     )
								self.assertEqual(str(x * 3.0     )       ,					'''(3.0,6.0,9.0)'''     )
								self.assertEqual((a * b)       ,					0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(zero_vector(1_0     )     ).count('''0'''     )       ,					1_0     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(str(unit_basis_vector(3       ,					1     )     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Vector([1, 2, 3]     )
								__magic_name__		:List[Any]        =      Vector([1, 0, 1]     )
								self.assertEqual(str(axpy(2       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )     )       ,					'''(3,4,7)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0, 0, 0, 0]     )
								__magic_name__		:Optional[int]        =      x.copy()
								self.assertEqual(str(__lowerCAmelCase     )       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Vector([1, 0, 0]     )
								x.change_component(0       ,					0     )
								x.change_component(1       ,					1     )
								self.assertEqual(str(__lowerCAmelCase     )       ,					'''(0,1,0)'''     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual('''|1,2,3|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:List[str]        =      [[-3, -1_4, -1_0], [-5, -1_0, -5], [-2, -1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(minors[x][y]       ,					a.minor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Any        =      [[-3, 1_4, -1_0], [5, -1_0, 5], [-2, 1, 0]]
								for x in range(a.height()     ):
												for y in range(a.width()     ):
																self.assertEqual(cofactors[x][y]       ,					a.cofactor(__lowerCAmelCase       ,					__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(-5       ,					a.determinant()     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]       ,					3       ,					3     )
								__magic_name__		:Any        =      Vector([1, 2, 3]     )
								self.assertEqual('''(14,32,50)'''       ,					str(a * x     )     )
								self.assertEqual('''|2,4,6|\n|8,10,12|\n|14,16,18|\n'''       ,					str(a * 2     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								a.change_component(0       ,					2       ,					5     )
								self.assertEqual('''|1,2,5|\n|2,4,5|\n|6,7,8|\n'''       ,					str(__lowerCAmelCase     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								self.assertEqual(7       ,					a.component(2       ,					1     )       ,					0.01     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Optional[Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|2,4,10|\n|4,8,10|\n|12,14,18|\n'''       ,					str(a + b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]       ,					3       ,					3     )
								__magic_name__		:Union[str, Any]        =      Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 1_0]]       ,					3       ,					3     )
								self.assertEqual('''|0,0,-4|\n|0,0,0|\n|0,0,-2|\n'''       ,					str(a - b     )     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.assertEqual(
								    '''|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n'''       ,					str(square_zero_matrix(5     )     )       ,					)


if __name__ == "__main__":
					unittest.main()






