


import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
    from transformers import MarkupLMFeatureExtractor



class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   def __init__(  self  : Optional[int]       ,  _lowerCamelCase  : List[str]							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						parent


   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         return {}





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						"""<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR=\"FFFFFF\">
    <HR>
    <a href=\"http://google.com\">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style=\"color:#0000FF\">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>"""

      __lowerCamelCase     :      Dict       						=						"""
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    """

      return [html_string_a, html_string_a]




@require_bsa
class 							_UpperCamelCase (						A,unittest.TestCase						):

   '''simple docstring'''







   a_				:							int    = MarkupLMFeatureExtractor if is_bsa_available() else None

   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						MarkupLMFeatureExtractionTester(self							)

   @property
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         return self.feature_extract_tester.prepare_feat_extract_dict()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.feature_extraction_class()

         # Test not batched input
         __lowerCamelCase     :      Dict       						=						get_html_strings()[0]
         __lowerCamelCase     :      List[Any]       						=						feature_extractor(_lowerCamelCase							)

         # fmt: off
         __lowerCamelCase     :      int       						=						[["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
         __lowerCamelCase     :      Any       						=						[["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
         # fmt: on

         self.assertEqual(encoding.nodes       ,  _lowerCamelCase							)
         self.assertEqual(encoding.xpaths       ,  _lowerCamelCase							)

         # Test batched
         __lowerCamelCase     :      List[str]       						=						get_html_strings()
         __lowerCamelCase     :      str       						=						feature_extractor(_lowerCamelCase							)

         # fmt: off
         __lowerCamelCase     :      Any       						=						expected_nodes + [["""My First Heading""", """My first paragraph."""]]
         __lowerCamelCase     :      Optional[int]       						=						expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]

         self.assertEqual(len(encoding.nodes							)       ,  2							)
         self.assertEqual(len(encoding.xpaths							)       ,  2							)

         self.assertEqual(encoding.nodes       ,  _lowerCamelCase							)
         self.assertEqual(encoding.xpaths       ,  _lowerCamelCase							)







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


__UpperCamelCase    :						Tuple     =							{
    '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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						["""layers""", """blocks"""]
      for k in ignore_keys:
            state_dict.pop(UpperCAmelCase						,	UpperCAmelCase      )


__UpperCamelCase    :						str     =							{
    '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   _UpperCAmelCase       (     UpperCAmelCase      :			Dict      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						list(s_dict.keys()      )
      for key in keys:
            __lowerCamelCase     :      Union[str, Any]       						=						key
            for k, v in WHISPER_MAPPING.items():
                  if k in key:
                        __lowerCamelCase     :      Optional[int]       						=						new_key.replace(UpperCAmelCase						,	UpperCAmelCase      )

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

            __lowerCamelCase     :      Dict       						=						s_dict.pop(UpperCAmelCase      )
      return s_dict





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						emb.weight.shape
      __lowerCamelCase     :      Tuple       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						emb.weight.data
      return lin_layer





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			str      ):
      """simple docstring"""




      os.makedirs(UpperCAmelCase						,	exist_ok=UpperCAmelCase      )
      __lowerCamelCase     :      Any       						=						os.path.basename(UpperCAmelCase      )

      __lowerCamelCase     :      int       						=						url.split("""/"""      )[-2]
      __lowerCamelCase     :      str       						=						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=1_024      ) as loop:
                  while True:
                        __lowerCamelCase     :      Union[str, Any]       						=						source.read(8_192      )
                        if not buffer:
                              break

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

      __lowerCamelCase     :      List[str]       						=						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   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[int]      ):
      """simple docstring"""




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

      __lowerCamelCase     :      Any       						=						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						, __lowerCamelCase     :      str       						=						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     :      Tuple       						=						make_linear_from_emb(model.model.decoder.embed_tokens      )
      else:
            __lowerCamelCase     :      Tuple       						=						proj_out_weights

      model.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Union[str, 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.')
    __UpperCamelCase    :						Tuple     =							parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



