
"""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


_snake_case          =						{
    '''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 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



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


_snake_case          =						{
    '''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 	__snake_case  (    SCREAMING_SNAKE_CASE:					int						):






							"""simple docstring"""



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

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

														_lowerCAmelCase  	=			s_dict.pop(SCREAMING_SNAKE_CASE						)
							return s_dict






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase  	=			emb.weight.shape
							_lowerCAmelCase  	=			nn.Linear(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  bias=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			emb.weight.data
							return lin_layer






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							os.makedirs(SCREAMING_SNAKE_CASE     ,  exist_ok=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			os.path.basename(SCREAMING_SNAKE_CASE						)

							_lowerCAmelCase  	=			url.split('/'						)[-2]
							_lowerCAmelCase  	=			os.path.join(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

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

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

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

							_lowerCAmelCase  	=			open(SCREAMING_SNAKE_CASE     ,  'rb'						).read()
							if hashlib.shaaaa(SCREAMING_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 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



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

							_lowerCAmelCase  	=			WhisperConfig(
							    vocab_size=dimensions['n_vocab']     ,  encoder_ffn_dim=SCREAMING_SNAKE_CASE     ,  decoder_ffn_dim=SCREAMING_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']     ,  )

							_lowerCAmelCase  	=			WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase	,		_lowerCAmelCase  	=			model.model.load_state_dict(SCREAMING_SNAKE_CASE     ,  strict=SCREAMING_SNAKE_CASE						)
							if len(SCREAMING_SNAKE_CASE						) > 0 and not set(SCREAMING_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:
														_lowerCAmelCase  	=			make_linear_from_emb(model.model.decoder.embed_tokens						)
							else:
														_lowerCAmelCase  	=			proj_out_weights

							model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	_snake_case          =						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.''')
	_snake_case          =						parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""



import pytest


_snake_case          =						'''__dummy_dataset1__'''

_snake_case          =						'''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def 	__snake_case  (    ):






							"""simple docstring"""



							return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			dataset_loading_script_name
							_lowerCAmelCase  	=			tmp_path / 'datasets' / script_name
							script_dir.mkdir(parents=SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			script_dir / f"""{script_name}.py"""
							with open(SCREAMING_SNAKE_CASE     ,  'w'						) as f:
														f.write(SCREAMING_SNAKE_CASE						)
							return str(SCREAMING_SNAKE_CASE						)






