



'''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


_lowerCAmelCase										=     {
    '''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= ["""layers""", """blocks"""]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase							,   UpperCamelCase      )


_lowerCAmelCase										=     {
    '''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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





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

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

        lowerCAmelCase__			:  Any											= s_dict.pop(UpperCamelCase      )
    return s_dict
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= emb.weight.shape
    lowerCAmelCase__			:  List[Any]											= nn.Linear(UpperCamelCase							,   UpperCamelCase							,   bias=UpperCamelCase      )
    lowerCAmelCase__			:  Optional[Any]											= emb.weight.data
    return lin_layer
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





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

    lowerCAmelCase__			:  Optional[Any]											= url.split("""/"""      )[-2]
    lowerCAmelCase__			:  Tuple											= 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      ):
        lowerCAmelCase__			:  Any											= 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=1024      ) as loop:
            while True:
                lowerCAmelCase__			:  Dict											= source.read(8192      )
                if not buffer:
                    break

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

    lowerCAmelCase__			:  Any											= 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        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





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

    lowerCAmelCase__			:  Tuple											= 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"""]							,   )

    lowerCAmelCase__			:  List[str]											= WhisperForConditionalGeneration(UpperCamelCase      )
    lowerCAmelCase__				,       lowerCAmelCase__			:  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:
        lowerCAmelCase__			:  str											= make_linear_from_emb(model.model.decoder.embed_tokens      )
    else:
        lowerCAmelCase__			:  str											= proj_out_weights

    model.save_pretrained(UpperCamelCase      )


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

 convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowerCAmelCase										=     10
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    for i in range(UpperCamelCase							,   UpperCamelCase      ):
        if array[i] == target:
            return i
    return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Optional[Any]											= 0
    lowerCAmelCase__			:  Optional[int]											= len(UpperCamelCase      )
    while left <= right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

        lowerCAmelCase__			:  Tuple											= (left + right) // 3 + 1
        lowerCAmelCase__			:  List[str]											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            lowerCAmelCase__			:  int											= one_third - 1
        elif array[two_third] < target:
            lowerCAmelCase__			:  Union[str, Any]											= two_third + 1

        else:
            lowerCAmelCase__			:  List[Any]											= one_third + 1
            lowerCAmelCase__			:  List[str]											= two_third - 1
    else:
        return -1
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    if left < right:
        if right - left < precision:
            return lin_search(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        lowerCAmelCase__			:  Dict											= (left + right) // 3 + 1
        lowerCAmelCase__			:  Tuple											= 2 * (left + right) // 3 + 1

        if array[one_third] == target:
            return one_third
        elif array[two_third] == target:
            return two_third

        elif target < array[one_third]:
            return rec_ternary_search(UpperCamelCase							,   one_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
        elif array[two_third] < target:
            return rec_ternary_search(two_third + 1							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
        else:
            return rec_ternary_search(one_third + 1							,   two_third - 1							,   UpperCamelCase							,   UpperCamelCase      )
    else:
        return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _lowerCAmelCase										=     input('''Enter numbers separated by comma:\n''').strip()
 _lowerCAmelCase										=     [int(item.strip()) for item in user_input.split(''',''')]
 assert collection == sorted(collection), F"List must be ordered.\n{collection}."
 _lowerCAmelCase										=     int(input('''Enter the number to be found in the list:\n''').strip())
 _lowerCAmelCase										=     ite_ternary_search(collection, target)
 _lowerCAmelCase										=     rec_ternary_search(0, len(collection) - 1, collection, target)
 if resulta != -1:
  print(F"""Iterative search: {target} found at positions: {resulta}""")
  print(F"""Recursive search: {target} found at positions: {resulta}""")
 else:
  print('''Not found''')


