





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


lowercase      :       List[Any]									=  {
    """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_	(		A__				)  ->		Any:
       a__						:	List[str] =				['layers', 'blocks']
       for k in ignore_keys:
              state_dict.pop(A__		,      A__				)


lowercase      :       int									=  {
    """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_	(		A__				)  ->		int:
       a__						:	Any =				list(s_dict.keys()				)
       for key in keys:
              a__						:	Optional[Any] =				key
              for k, v in WHISPER_MAPPING.items():
                     if k in key:
                            a__						:	Union[str, Any] =				new_key.replace(A__		,      A__				)

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

              a__						:	int =				s_dict.pop(A__				)
       return s_dict



def A_	(		A__				)  ->		Optional[int]:
       a__ ,							a__						:	List[str] =				emb.weight.shape
       a__						:	Tuple =				nn.Linear(A__		,      A__		,      bias=A__				)
       a__						:	Any =				emb.weight.data
       return lin_layer



def A_	(		A__		,      A__				)  ->		bytes:
       os.makedirs(A__		,      exist_ok=A__				)
       a__						:	Union[str, Any] =				os.path.basename(A__				)

       a__						:	Any =				url.split('/'				)[-2]
       a__						:	Dict =				os.path.join(A__		,      A__				)

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

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

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

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

       a__						:	str =				WhisperConfig(
           vocab_size=dimensions['n_vocab']		,      encoder_ffn_dim=A__		,      decoder_ffn_dim=A__		,      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__						:	int =				WhisperForConditionalGeneration(A__				)
       a__ ,							a__						:	Dict =				model.model.load_state_dict(A__		,      strict=A__				)
       if len(A__				) > 0 and not set(A__				) <= {
           "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__						:	Any =				make_linear_from_emb(model.model.decoder.embed_tokens				)
       else:
              a__						:	Union[str, Any] =				proj_out_weights

       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       int									=  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.""")
    lowercase      :       List[str]									=  parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
    import torch

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm


lowercase      :       List[str]									=  logging.get_logger(__name__)





@dataclass
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]

   def __init__(					self					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                        a__						:	Union[str, Any] =				deprecated_arg[3:]
                        setattr(self					,  lowercase					,  not kwargs.pop(lowercase))
                        logger.warning(
                            F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                            F' {positive_arg}={kwargs[positive_arg]}')

          a__						:	Union[str, Any] =				kwargs.pop('torchscript'					,  self.torchscript)
          a__						:	Tuple =				kwargs.pop('torch_xla_tpu_print_metrics'					,  self.torch_xla_tpu_print_metrics)
          a__						:	Tuple =				kwargs.pop('fp16_opt_level'					,  self.fpaa_opt_level)
          super().__init__(**lowercase)

   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Trace the models using torchscript'''}	)
   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}	)
   __A   :				str         =  field(
       default='''O1''' ,  metadata={
           '''help''': (
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           )
       } ,  )

   @cached_property
   def 							__lowercase		(					self)			->						Tuple["torch.device", int]:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          logger.info('PyTorch: setting up devices')
          if not self.cuda:
                 a__						:	List[str] =				torch.device('cpu')
                 a__						:	Optional[Any] =				0
          elif is_torch_tpu_available():
                 a__						:	List[str] =				xm.xla_device()
                 a__						:	Union[str, Any] =				0
          else:
                 a__						:	List[Any] =				torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                 a__						:	Optional[int] =				torch.cuda.device_count()
          return device, n_gpu

   @property
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          return is_torch_tpu_available() and self.tpu

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          # TODO(PVP): currently only single GPU is supported
          return torch.cuda.current_device()

   @property
   def 							__lowercase		(					self)			->						"torch.device":







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[0]

   @property
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[1]

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return self.n_gpu > 0


