






'''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    lowercase__			(				__UpperCamelCase   )->			int:
      UpperCamelCase      = ['''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    lowercase__			(				__UpperCamelCase   )->			Any:
      UpperCamelCase      = list(s_dict.keys()   )
      for key in keys:
            UpperCamelCase      = key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        UpperCamelCase      = new_key.replace(__UpperCamelCase		,						__UpperCamelCase   )

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

            UpperCamelCase      = s_dict.pop(__UpperCamelCase   )
      return s_dict







def    lowercase__			(				__UpperCamelCase   )->			Optional[int]:
      UpperCamelCase      = emb.weight.shape
      UpperCamelCase      = nn.Linear(__UpperCamelCase		,						__UpperCamelCase		,						bias=__UpperCamelCase   )
      UpperCamelCase      = emb.weight.data
      return lin_layer







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			bytes:
      os.makedirs(__UpperCamelCase		,						exist_ok=__UpperCamelCase   )
      UpperCamelCase      = os.path.basename(__UpperCamelCase   )

      UpperCamelCase      = url.split("""/"""   )[-2]
      UpperCamelCase      = 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   ):
            UpperCamelCase      = 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:
                        UpperCamelCase      = source.read(8192   )
                        if not buffer:
                              break

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

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

      UpperCamelCase      = 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"""]		,						)

      UpperCamelCase      = WhisperForConditionalGeneration(__UpperCamelCase   )
      UpperCamelCase      = 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:
            UpperCamelCase      = make_linear_from_emb(model.model.decoder.embed_tokens   )
      else:
            UpperCamelCase      = 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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__     =       {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE__     =       [
         'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
         'NllbMoeForConditionalGeneration',
         'NllbMoeModel',
         'NllbMoePreTrainedModel',
         'NllbMoeTop2Router',
         'NllbMoeSparseMLP',
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     SCREAMING_SNAKE_CASE__     =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
