"""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


_a					:					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   __UpperCAmelCase    (   UpperCAmelCase_   :    str  )							->      int:

							'''simple docstring'''





							__snake_case      :							List[Any]						=			['layers', 'blocks']
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase_      ,							UpperCAmelCase_  )


_a					:					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   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]  )							->      List[str]:

							'''simple docstring'''





							__snake_case      :							Union[str, Any]						=			list(s_dict.keys()  )
							for key in keys:
														__snake_case      :							Tuple						=			key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												__snake_case      :							Optional[int]						=			new_key.replace(UpperCAmelCase_      ,							UpperCAmelCase_  )

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

														__snake_case      :							str						=			s_dict.pop(UpperCAmelCase_  )
							return s_dict
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]  )							->      List[str]:

							'''simple docstring'''





							__snake_case		,    __snake_case      :							List[str]						=			emb.weight.shape
							__snake_case      :							Dict						=			nn.Linear(UpperCAmelCase_      ,							UpperCAmelCase_      ,							bias=UpperCAmelCase_  )
							__snake_case      :							List[str]						=			emb.weight.data
							return lin_layer
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    str  )							->      bytes:

							'''simple docstring'''





							os.makedirs(UpperCAmelCase_      ,							exist_ok=UpperCAmelCase_  )
							__snake_case      :							Optional[int]						=			os.path.basename(UpperCAmelCase_  )

							__snake_case      :							str						=			url.split('/'  )[-2]
							__snake_case      :							List[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      :							Tuple						=			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=10_24  ) as loop:
																					while True:
																												__snake_case      :							List[Any]						=			source.read(81_92  )
																												if not buffer:
																																			break

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

							__snake_case      :							Optional[int]						=			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_   :    List[str]      ,							UpperCAmelCase_   :    Optional[int]  )							->      List[Any]:

							'''simple docstring'''





							if ".pt" not in checkpoint_path:
														__snake_case      :							str						=			_download(_MODELS[checkpoint_path]  )
							else:
														__snake_case      :							List[str]						=			torch.load(UpperCAmelCase_      ,							map_location='cpu'  )
							__snake_case      :							Optional[int]						=			original_checkpoint['dims']
							__snake_case      :							List[Any]						=			original_checkpoint['model_state_dict']
							__snake_case      :							Optional[int]						=			state_dict['decoder.token_embedding.weight']
							remove_ignore_keys_(UpperCAmelCase_  )
							rename_keys(UpperCAmelCase_  )
							__snake_case      :							List[str]						=			True
							__snake_case      :							List[str]						=			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      :							List[Any]						=			WhisperForConditionalGeneration(UpperCAmelCase_  )
							__snake_case		,    __snake_case      :							Optional[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      :							int						=			make_linear_from_emb(model.model.decoder.embed_tokens  )
							else:
														__snake_case      :							Union[str, Any]						=			proj_out_weights

							model.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
	_a					:					Optional[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.")
	_a					:					int=			parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]  )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]  )
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()





