


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    :   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     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Optional[Any]										=	['''layers''', '''blocks''']
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase_			,  UpperCAmelCase_						)


snake_case    :   Optional[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     __lowerCamelCase						(			UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:List[Any]										=	list(s_dict.keys()						)
      for key in keys:
            a						:Tuple										=	key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        a						:List[Any]										=	new_key.replace(UpperCAmelCase_			,  UpperCAmelCase_						)

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

            a						:Dict										=	s_dict.pop(UpperCAmelCase_						)
      return s_dict
def     __lowerCamelCase						(			UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:Dict										=	emb.weight.shape
      a						:List[Any]										=	nn.Linear(UpperCAmelCase_			,  UpperCAmelCase_			,  bias=UpperCAmelCase_						)
      a						:Dict										=	emb.weight.data
      return lin_layer
def     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      os.makedirs(UpperCAmelCase_			,  exist_ok=UpperCAmelCase_						)
      a						:List[Any]										=	os.path.basename(UpperCAmelCase_						)

      a						:Union[str, Any]										=	url.split('''/'''						)[-2]
      a						:Union[str, Any]										=	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_						):
            a						:Dict										=	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:
                        a						:Optional[int]										=	source.read(8192						)
                        if not buffer:
                              break

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

      a						:List[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     __lowerCamelCase						(			UpperCAmelCase_						:		str			,  UpperCAmelCase_						:		List[Any]						):




      """simple docstring"""





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

      a						:List[str]										=	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''']			,  )

      a						:List[Any]										=	WhisperForConditionalGeneration(UpperCAmelCase_						)
      a						:Optional[int]										=	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:
            a						:List[str]										=	make_linear_from_emb(model.model.decoder.embed_tokens						)
      else:
            a						:List[str]										=	proj_out_weights

      model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
     snake_case    :   List[Any]	    =					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    :   str	    =					parser.parse_args()

     convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    '''pipelines_utils''',
    '''0.22.0''',
    '''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
    standard_warn=False,
    stacklevel=3,
)





