
'''simple docstring'''


import argparse
from pathlib import Path

import torch

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


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



def  __snake_case     (    _UpperCAmelCase					:       List[str]):
   UpperCamelCase       =  torch.load(_UpperCAmelCase,      map_location='''cpu''')
   if "model" in sd.keys():
      UpperCamelCase       =  torch.load(_UpperCAmelCase,      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(_UpperCAmelCase)

   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(_UpperCAmelCase)

   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(_UpperCAmelCase,      depth // 3,      dim=0)

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

   return sd



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Optional[Any]=None):
   UpperCamelCase       =  load_checkpoint(_UpperCAmelCase)

   if config is not None:
      UpperCamelCase       =  OPTConfig.from_pretrained(_UpperCAmelCase)
   else:
      UpperCamelCase       =  OPTConfig()

   UpperCamelCase       =  OPTModel(_UpperCAmelCase).half().eval()
   model.load_state_dict(_UpperCAmelCase)

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


if __name__ == "__main__":
   snake_case_ :  List[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_ :  Union[str, Any]     =					parser.parse_args()
   convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)







'''simple docstring'''


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_ :  Tuple     =					get_tests_dir('fixtures/test_sentencepiece_no_bos.model')


@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/pegasus-large'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''</s>'''
      UpperCamelCase       =  1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__   ) ,   lowerCamelCase__   )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__   ) ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  list(self.get_tokenizer().get_vocab().keys()   )

      self.assertEqual(vocab_keys[0] ,   '''<pad>'''   )
      self.assertEqual(vocab_keys[1] ,   '''</s>'''   )
      self.assertEqual(vocab_keys[-1] ,   '''v'''   )
      self.assertEqual(len(lowerCamelCase__   ) ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.assertEqual(self.get_tokenizer().vocab_size ,   1_1_0_3   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
          ''' </s> <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # <mask_1> masks whole sentence while <mask_2> masks single word
      UpperCamelCase       =  '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
      UpperCamelCase       =  [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self._large_tokenizer
      # The tracebacks for the following asserts are **better** without messages or self.assertEqual
      assert tokenizer.vocab_size == 9_6_1_0_3
      assert tokenizer.pad_token_id == 0
      assert tokenizer.eos_token_id == 1
      assert tokenizer.offset == 1_0_3
      assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
      assert tokenizer.unk_token == "<unk>"
      assert tokenizer.model_max_length == 1_0_2_4
      UpperCamelCase       =  '''To ensure a smooth flow of bank resolutions.'''
      UpperCamelCase       =  [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
      UpperCamelCase       =  tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )
      assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_5_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 1_0_2_4)
      assert batch.attention_mask.shape == (2, 1_0_2_4)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   @slow
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  {'''input_ids''': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=lowerCamelCase__ ,   model_name='''google/bigbird-pegasus-large-arxiv''' ,   revision='''ba85d0851d708441f91440d509690f1ab6353415''' ,   )




@require_sentencepiece
@require_tokenizers
class 						lowercase__	(  snake_case_,					unittest.TestCase   ):






   '''simple docstring'''







   _snake_case		   =							PegasusTokenizer
   _snake_case		   =							PegasusTokenizerFast
   _snake_case		   =							True
   _snake_case		   =							True







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      super().setUp()

      # We have a SentencePiece fixture for testing
      UpperCamelCase       =  PegasusTokenizer(lowerCamelCase__ ,   offset=0 ,   mask_token_sent=lowerCamelCase__ ,   mask_token='''[MASK]'''   )
      tokenizer.save_pretrained(self.tmpdirname   )







   @cached_property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''   )







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return PegasusTokenizer.from_pretrained(self.tmpdirname ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      return ("This is a test", "This is a test")







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  self.tokenizer_class.from_pretrained(self.tmpdirname   )
      UpperCamelCase       =  (
          '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
          ''' <pad> <pad> <pad>'''
      )
      UpperCamelCase       =  rust_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      UpperCamelCase       =  py_tokenizer([raw_input_str] ,   return_tensors=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__   ).input_ids[0]
      self.assertListEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   @require_torch
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  ['''This is going to be way too long.''' * 1_0_0_0, '''short example''']
      UpperCamelCase       =  ['''not super long but more than 5 tokens''', '''tiny''']
      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )
      UpperCamelCase       =  self._large_tokenizer(
          text_target=lowerCamelCase__ ,   max_length=5 ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   return_tensors='''pt'''   )

      assert batch.input_ids.shape == (2, 4_0_9_6)
      assert batch.attention_mask.shape == (2, 4_0_9_6)
      assert targets["input_ids"].shape == (2, 5)
      assert len(lowerCamelCase__   ) == 2  # input_ids, attention_mask.







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  (
          '''This is an example string that is used to test the original TF implementation against the HF'''
          ''' implementation'''
      )

      UpperCamelCase       =  self._large_tokenizer(lowerCamelCase__   ).input_ids

      self.assertListEqual(
          lowerCamelCase__ ,   [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] ,   )





