






import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__      :	Optional[Any]       	=      logging.get_logger(__name__)






def     _snake_case    (__lowercase):
   UpperCamelCase_									=     torch.load(__lowercase    ,      map_location='cpu')
   if "model" in sd.keys():
      UpperCamelCase_									=     torch.load(__lowercase    ,      map_location='cpu')['model']

   # pop unnecessary weights
   UpperCamelCase_									=     [
       'decoder.version',
       'decoder.output_projection.weight',
   ]
   for key in keys_to_delete:
      if key in sd:
         sd.pop(__lowercase)

   UpperCamelCase_									=     {
       'decoder.project_in_dim.weight': 'decoder.project_in.weight',
       'decoder.project_out_dim.weight': 'decoder.project_out.weight',
       'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
       'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
   }
   for old_key, new_key in keys_to_rename.items():
      if old_key in sd:
         UpperCamelCase_									=     sd.pop(__lowercase)

   UpperCamelCase_									=     list(sd.keys())
   for key in keys:
      if ".qkv_proj." in key:
         UpperCamelCase_									=     sd[key]
         # We split QKV in separate Q,K,V

         UpperCamelCase_									=     key.replace('.qkv_proj.'    ,      '.q_proj.')
         UpperCamelCase_									=     key.replace('.qkv_proj.'    ,      '.k_proj.')
         UpperCamelCase_									=     key.replace('.qkv_proj.'    ,      '.v_proj.')

         UpperCamelCase_									=     value.shape[0]
         assert depth % 3 == 0
         # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
         # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
         UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     torch.split(__lowercase    ,      depth // 3    ,      dim=0)

         UpperCamelCase_									=     q
         UpperCamelCase_									=     k
         UpperCamelCase_									=     v
         del sd[key]

   return sd






@torch.no_grad()
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase=None):
   UpperCamelCase_									=     load_checkpoint(__lowercase)

   if config is not None:
      UpperCamelCase_									=     OPTConfig.from_pretrained(__lowercase)
   else:
      UpperCamelCase_									=     OPTConfig()

   UpperCamelCase_									=     OPTModel(__lowercase).half().eval()
   model.load_state_dict(__lowercase)

   # Check results
   Path(__lowercase).mkdir(exist_ok=__lowercase)
   model.save_pretrained(__lowercase)


if __name__ == "__main__":
 snake_case__      :	Union[str, Any]       	=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--fairseq_path""",
     type=str,
     help=(
         """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
         """ https://huggingface.co/models?other=opt_metasq"""
     ),
 )
 parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
 snake_case__      :	Optional[Any]       	=      parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)













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 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     parent




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {}






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

   UpperCamelCase_									=     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]


@require_bsa
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   MarkupLMFeatureExtractor if is_bsa_available() else None




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     MarkupLMFeatureExtractionTester(self )




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     return self.feature_extract_tester.prepare_feat_extract_dict()




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Initialize feature_extractor
     UpperCamelCase_									=     self.feature_extraction_class()

     # Test not batched input
     UpperCamelCase_									=     get_html_strings()[0]
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     [['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']]
     UpperCamelCase_									=     [['/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		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )

     # Test batched
     UpperCamelCase_									=     get_html_strings()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     expected_nodes + [['My First Heading', 'My first paragraph.']]
     UpperCamelCase_									=     expected_xpaths + [['/html/body/h1', '/html/body/p']]

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

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





