





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


SCREAMING_SNAKE_CASE_						   =       {
    """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   (      _SCREAMING_SNAKE_CASE	) ->   Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)


SCREAMING_SNAKE_CASE_						   =       {
    """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   (      _SCREAMING_SNAKE_CASE	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						list(s_dict.keys()	)
      for key in keys:
            SCREAMING_SNAKE_CASE		=						key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        SCREAMING_SNAKE_CASE		=						new_key.replace(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

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

            SCREAMING_SNAKE_CASE		=						s_dict.pop(_SCREAMING_SNAKE_CASE	)
      return s_dict



def    __lowercase   (      _SCREAMING_SNAKE_CASE	) ->   Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						emb.weight.shape
      SCREAMING_SNAKE_CASE		=						nn.Linear(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						bias=_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						emb.weight.data
      return lin_layer



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   List[Any]:


      '''simple docstring'''


      os.makedirs(_SCREAMING_SNAKE_CASE ,						exist_ok=_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						os.path.basename(_SCREAMING_SNAKE_CASE	)

      SCREAMING_SNAKE_CASE		=						url.split("""/"""	)[-2]
      SCREAMING_SNAKE_CASE		=						os.path.join(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

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

      if os.path.isfile(_SCREAMING_SNAKE_CASE	):
            SCREAMING_SNAKE_CASE		=						open(_SCREAMING_SNAKE_CASE ,						"""rb"""	).read()
            if hashlib.shaaaa(_SCREAMING_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(_SCREAMING_SNAKE_CASE	) as source, open(_SCREAMING_SNAKE_CASE ,						"""wb"""	) as output:
            with tqdm(
                total=int(source.info().get("""Content-Length"""	)	) ,						ncols=80 ,						unit="""iB""" ,						unit_scale=_SCREAMING_SNAKE_CASE ,						unit_divisor=10_24	) as loop:
                  while True:
                        SCREAMING_SNAKE_CASE		=						source.read(81_92	)
                        if not buffer:
                              break

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

      SCREAMING_SNAKE_CASE		=						open(_SCREAMING_SNAKE_CASE ,						"""rb"""	).read()
      if hashlib.shaaaa(_SCREAMING_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    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      if ".pt" not in checkpoint_path:
            SCREAMING_SNAKE_CASE		=						_download(_MODELS[checkpoint_path]	)
      else:
            SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE ,						map_location="""cpu"""	)
      SCREAMING_SNAKE_CASE		=						original_checkpoint["""dims"""]
      SCREAMING_SNAKE_CASE		=						original_checkpoint["""model_state_dict"""]
      SCREAMING_SNAKE_CASE		=						state_dict["""decoder.token_embedding.weight"""]
      remove_ignore_keys_(_SCREAMING_SNAKE_CASE	)
      rename_keys(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						True
      SCREAMING_SNAKE_CASE		=						state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

      SCREAMING_SNAKE_CASE		=						WhisperConfig(
          vocab_size=dimensions["""n_vocab"""] ,						encoder_ffn_dim=_SCREAMING_SNAKE_CASE ,						decoder_ffn_dim=_SCREAMING_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"""] ,						)

      SCREAMING_SNAKE_CASE		=						WhisperForConditionalGeneration(_SCREAMING_SNAKE_CASE	)
      SCREAMING_SNAKE_CASE		=						model.model.load_state_dict(_SCREAMING_SNAKE_CASE ,						strict=_SCREAMING_SNAKE_CASE	)
      if len(_SCREAMING_SNAKE_CASE	) > 0 and not set(_SCREAMING_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:
            SCREAMING_SNAKE_CASE		=						make_linear_from_emb(model.model.decoder.embed_tokens	)
      else:
            SCREAMING_SNAKE_CASE		=						proj_out_weights

      model.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       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.""")
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




