



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__			:  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__			:  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'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


