
"""simple docstring"""

from sklearn.metrics import matthews_corrcoef

import datasets


_A						=			"\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

_A						=			"\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

_A						=			"\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,	_KWARGS_DESCRIPTION		)
class 	_lowercase       (    datasets.Metric		):




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			return datasets.MetricInfo(
			    description=_DESCRIPTION		,			citation=_CITATION		,			inputs_description=_KWARGS_DESCRIPTION		,			features=datasets.Features(
			        {
			            'predictions': datasets.Value('int32' ),
			            'references': datasets.Value('int32' ),
			        } )		,			reference_urls=[
			        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
			    ]		,			)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Optional[int]:
			return {
			    "matthews_correlation": float(matthews_corrcoef(lowerCamelCase_		,			lowerCamelCase_		,			sample_weight=lowerCamelCase_ ) ),
			}







"""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						=			{
    '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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      str          =   ['layers', 'blocks']
		for k in ignore_keys:
				state_dict.pop(a_,						a_      )


_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 (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   list(s_dict.keys()      )
		for key in keys:
				lowerCamelCase					:      List[Any]          =   key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								lowerCamelCase					:      Optional[int]          =   new_key.replace(a_,						a_      )

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

				lowerCamelCase					:      Any          =   s_dict.pop(a_      )
		return s_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase     ,   lowerCamelCase					:      int          =   emb.weight.shape
		lowerCamelCase					:      Dict          =   nn.Linear(a_,						a_,						bias=a_      )
		lowerCamelCase					:      Union[str, Any]          =   emb.weight.data
		return lin_layer
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		os.makedirs(a_,						exist_ok=a_      )
		lowerCamelCase					:      Union[str, Any]          =   os.path.basename(a_      )

		lowerCamelCase					:      Any          =   url.split('/'      )[-2]
		lowerCamelCase					:      Tuple          =   os.path.join(a_,						a_      )

		if os.path.exists(a_      ) and not os.path.isfile(a_      ):
				raise RuntimeError(F"""{download_target} exists and is not a regular file"""      )

		if os.path.isfile(a_      ):
				lowerCamelCase					:      Union[str, Any]          =   open(a_,						'rb'      ).read()
				if hashlib.shaaaa(a_      ).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(a_      ) as source, open(a_,						'wb'      ) as output:
				with tqdm(
				    total=int(source.info().get('Content-Length'      )      ),						ncols=80,						unit='iB',						unit_scale=a_,						unit_divisor=1024      ) as loop:
						while True:
								lowerCamelCase					:      Union[str, Any]          =   source.read(8192      )
								if not buffer:
										break

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

		lowerCamelCase					:      int          =   open(a_,						'rb'      ).read()
		if hashlib.shaaaa(a_      ).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 (		a_,						a_      ):





		'''simple docstring'''


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

		lowerCamelCase					:      Optional[int]          =   WhisperConfig(
		    vocab_size=dimensions['n_vocab'],						encoder_ffn_dim=a_,						decoder_ffn_dim=a_,						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'],						)

		lowerCamelCase					:      Union[str, Any]          =   WhisperForConditionalGeneration(a_      )
		lowerCamelCase     ,   lowerCamelCase					:      Optional[int]          =   model.model.load_state_dict(a_,						strict=a_      )
		if len(a_      ) > 0 and not set(a_      ) <= {
		    "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:
				lowerCamelCase					:      List[Any]          =   make_linear_from_emb(model.model.decoder.embed_tokens      )
		else:
				lowerCamelCase					:      Tuple          =   proj_out_weights

		model.save_pretrained(a_      )


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)






