


'''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


UpperCamelCase__    					=      {
    '''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 					a__       (					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		Union[str, Any]													=				['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase__  ,					lowerCAmelCase__						)


UpperCamelCase__    					=      {
    '''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 					a__       (					lowerCAmelCase__						)    ->	Tuple:
    UpperCAmelCase__			:		Tuple													=				list(s_dict.keys()						)
    for key in keys:
        UpperCAmelCase__			:		List[str]													=				key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                UpperCAmelCase__			:		int													=				new_key.replace(lowerCAmelCase__  ,					lowerCAmelCase__						)

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

        UpperCAmelCase__			:		Dict													=				s_dict.pop(lowerCAmelCase__						)
    return s_dict





def 					a__       (					lowerCAmelCase__						)    ->	Any:
    UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				emb.weight.shape
    UpperCAmelCase__			:		Union[str, Any]													=				nn.Linear(lowerCAmelCase__  ,					lowerCAmelCase__  ,					bias=lowerCAmelCase__						)
    UpperCAmelCase__			:		Union[str, Any]													=				emb.weight.data
    return lin_layer





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	bytes:
    os.makedirs(lowerCAmelCase__  ,					exist_ok=lowerCAmelCase__						)
    UpperCAmelCase__			:		List[Any]													=				os.path.basename(lowerCAmelCase__						)

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

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

    if os.path.isfile(lowerCAmelCase__						):
        UpperCAmelCase__			:		Optional[Any]													=				open(lowerCAmelCase__  ,					'''rb'''						).read()
        if hashlib.shaaaa(lowerCAmelCase__						).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(lowerCAmelCase__						) as source, open(lowerCAmelCase__  ,					'''wb'''						) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''						)						)  ,					ncols=80  ,					unit='''iB'''  ,					unit_scale=lowerCAmelCase__  ,					unit_divisor=10_24						) as loop:
            while True:
                UpperCAmelCase__			:		Dict													=				source.read(81_92						)
                if not buffer:
                    break

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

    UpperCAmelCase__			:		Union[str, Any]													=				open(lowerCAmelCase__  ,					'''rb'''						).read()
    if hashlib.shaaaa(lowerCAmelCase__						).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 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    if ".pt" not in checkpoint_path:
        UpperCAmelCase__			:		Optional[int]													=				_download(_MODELS[checkpoint_path]						)
    else:
        UpperCAmelCase__			:		int													=				torch.load(lowerCAmelCase__  ,					map_location='''cpu'''						)
    UpperCAmelCase__			:		Tuple													=				original_checkpoint['''dims''']
    UpperCAmelCase__			:		Optional[int]													=				original_checkpoint['''model_state_dict''']
    UpperCAmelCase__			:		Optional[int]													=				state_dict['''decoder.token_embedding.weight''']
    remove_ignore_keys_(lowerCAmelCase__						)
    rename_keys(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				True
    UpperCAmelCase__			:		str													=				state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

    UpperCAmelCase__			:		int													=				WhisperConfig(
        vocab_size=dimensions['''n_vocab''']  ,					encoder_ffn_dim=lowerCAmelCase__  ,					decoder_ffn_dim=lowerCAmelCase__  ,					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''']  ,					)

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

    model.save_pretrained(lowerCAmelCase__						)


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

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)




'''simple docstring'''
# Algorithm for the pigeonhole sorting







def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Any													=				min(lowerCAmelCase__						)  # min() finds the minimum value
    UpperCAmelCase__			:		Optional[int]													=				max(lowerCAmelCase__						)  # max() finds the maximum value

    UpperCAmelCase__			:		int													=				max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    UpperCAmelCase__			:		Any													=				[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(lowerCAmelCase__  ,					lowerCAmelCase__						), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    UpperCAmelCase__			:		Optional[int]													=				0
    for count in range(lowerCAmelCase__						):
        while holes[count] > 0:
            holes[count] -= 1
            UpperCAmelCase__			:		Dict													=				count + min_val
            i += 1





def 					a__       (					)    ->	Union[str, Any]:
    UpperCAmelCase__			:		List[str]													=				[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(lowerCAmelCase__						)
    print('''Sorted order is:'''  ,					''' '''.join(lowerCAmelCase__						)						)


if __name__ == "__main__":
  main()
