






"""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	:	List[str]				=      {
    "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__       (  snake_case       : Any				)->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							["layers", "blocks"]
     for k in ignore_keys:
          state_dict.pop(snake_case__			,					snake_case__				)


_lowerCAmelCase	:	List[Any]				=      {
    "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__       (  snake_case       : Union[str, Any]				)->		Optional[int]:






     '''simple docstring'''



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

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

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]				)->		List[str]:






     '''simple docstring'''



     UpperCAmelCase__							,				UpperCAmelCase__						:    str							=							emb.weight.shape
     UpperCAmelCase__						:    Union[str, Any]							=							nn.Linear(snake_case__			,					snake_case__			,					bias=snake_case__				)
     UpperCAmelCase__						:    Optional[Any]							=							emb.weight.data
     return lin_layer


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Any:






     '''simple docstring'''



     os.makedirs(snake_case__			,					exist_ok=snake_case__				)
     UpperCAmelCase__						:    List[Any]							=							os.path.basename(snake_case__				)

     UpperCAmelCase__						:    List[Any]							=							url.split("/"				)[-2]
     UpperCAmelCase__						:    Any							=							os.path.join(snake_case__			,					snake_case__				)

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

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

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

     UpperCAmelCase__						:    List[str]							=							open(snake_case__			,					"rb"				).read()
     if hashlib.shaaaa(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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : List[str]				)->		Union[str, Any]:






     '''simple docstring'''



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

     UpperCAmelCase__						:    Optional[Any]							=							WhisperConfig(
         vocab_size=dimensions["n_vocab"]			,					encoder_ffn_dim=snake_case__			,					decoder_ffn_dim=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"]			,					)

     UpperCAmelCase__						:    List[Any]							=							WhisperForConditionalGeneration(snake_case__				)
     UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, Any]							=							model.model.load_state_dict(snake_case__			,					strict=snake_case__				)
     if len(snake_case__				) > 0 and not set(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:
          UpperCAmelCase__						:    str							=							make_linear_from_emb(model.model.decoder.embed_tokens				)
     else:
          UpperCAmelCase__						:    Tuple							=							proj_out_weights

     model.save_pretrained(snake_case__				)


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

 convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


