





'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 					__snake_case  (					__SCREAMING_SNAKE_CASE		,				unittest.TestCase							):






   '''simple docstring'''





   lowerCamelCase__    							=      LEDTokenizer
   lowerCamelCase__    							=      LEDTokenizerFast
   lowerCamelCase__    							=      True

   def 		__UpperCamelCase						(							self       ):
    super().setUp()
    snake_case__     :					int    =  [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
    ]
    snake_case__     :					int    =  dict(zip(__SCREAMING_SNAKE_CASE	,		range(len(__SCREAMING_SNAKE_CASE       )       )       )       )
    snake_case__     :					Optional[Any]    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    snake_case__     :					List[Any]    =  {"""unk_token""": """<unk>"""}

    snake_case__     :					Optional[Any]    =  os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""vocab_file"""]       )
    snake_case__     :					Dict    =  os.path.join(self.tmpdirname	,		VOCAB_FILES_NAMES["""merges_file"""]       )
    with open(self.vocab_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + """\n"""       )
    with open(self.merges_file	,		"""w"""	,		encoding="""utf-8"""       ) as fp:
     fp.write("""\n""".join(__SCREAMING_SNAKE_CASE       )       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    kwargs.update(self.special_tokens_map       )
    return self.tokenizer_class.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		**__SCREAMING_SNAKE_CASE       ):
    kwargs.update(self.special_tokens_map       )
    return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,		**__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    return "lower newer", "lower newer"

   @cached_property
   def 		__UpperCamelCase						(							self       ):
    return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""       )

   @cached_property
   def 		__UpperCamelCase						(							self       ):
    return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Optional[int]    =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    snake_case__     :					Optional[int]    =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					Any    =  tokenizer(__SCREAMING_SNAKE_CASE	,		max_length=len(__SCREAMING_SNAKE_CASE       )	,		padding=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )
     self.assertIsInstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

     self.assertEqual((2, 9)	,		batch.input_ids.shape       )
     self.assertEqual((2, 9)	,		batch.attention_mask.shape       )
     snake_case__     :					Optional[Any]    =  batch.input_ids.tolist()[0]
     self.assertListEqual(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					List[Any]    =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					Dict    =  tokenizer(__SCREAMING_SNAKE_CASE	,		padding=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )
     self.assertIn("""input_ids"""	,		__SCREAMING_SNAKE_CASE       )
     self.assertIn("""attention_mask"""	,		__SCREAMING_SNAKE_CASE       )
     self.assertNotIn("""labels"""	,		__SCREAMING_SNAKE_CASE       )
     self.assertNotIn("""decoder_attention_mask"""	,		__SCREAMING_SNAKE_CASE       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Union[str, Any]    =  [
        """Summary of the text.""",
        """Another summary.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					Union[str, Any]    =  tokenizer(text_target=__SCREAMING_SNAKE_CASE	,		max_length=3_2	,		padding="""max_length"""	,		return_tensors="""pt"""       )
     self.assertEqual(3_2	,		targets["""input_ids"""].shape[1]       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					List[str]    =  tokenizer(
         ["""I am a small frog""" * 1_0_2_4, """I am a small frog"""]	,		padding=__SCREAMING_SNAKE_CASE	,		truncation=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )
     self.assertIsInstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       )
     self.assertEqual(batch.input_ids.shape	,		(2, 5_1_2_2)       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  ["""A long paragraph for summarization."""]
    snake_case__     :					List[Any]    =  [
        """Summary of the text.""",
    ]
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					Any    =  tokenizer(__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )
     snake_case__     :					int    =  tokenizer(text_target=__SCREAMING_SNAKE_CASE	,		return_tensors="""pt"""       )
     snake_case__     :					List[Any]    =  inputs["""input_ids"""]
     snake_case__     :					int    =  targets["""input_ids"""]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )

   @require_torch
   def 		__UpperCamelCase						(							self       ):
    for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     snake_case__     :					Dict    =  ["""Summary of the text.""", """Another summary."""]
     snake_case__     :					List[str]    =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

     snake_case__     :					Optional[Any]    =  tokenizer(__SCREAMING_SNAKE_CASE	,		padding=__SCREAMING_SNAKE_CASE       )
     snake_case__     :					Any    =  [[0] * len(__SCREAMING_SNAKE_CASE       ) for x in encoded_output["""input_ids"""]]
     snake_case__     :					Optional[int]    =  tokenizer.pad(__SCREAMING_SNAKE_CASE       )
     self.assertSequenceEqual(outputs["""global_attention_mask"""]	,		__SCREAMING_SNAKE_CASE       )

   def 		__UpperCamelCase						(							self       ):
    pass


   def 		__UpperCamelCase						(							self       ):
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
      snake_case__     :					str    =  self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Any    =  self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
      snake_case__     :					Optional[Any]    =  """A, <mask> AllenNLP sentence."""
      snake_case__     :					List[str]    =  tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE	,		return_token_type_ids=__SCREAMING_SNAKE_CASE       )
      snake_case__     :					List[str]    =  tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE	,		add_special_tokens=__SCREAMING_SNAKE_CASE	,		return_token_type_ids=__SCREAMING_SNAKE_CASE       )
      self.assertEqual(sum(tokens_r["""token_type_ids"""]       )	,		sum(tokens_p["""token_type_ids"""]       )       )
      self.assertEqual(
          sum(tokens_r["""attention_mask"""]       ) / len(tokens_r["""attention_mask"""]       )	,		sum(tokens_p["""attention_mask"""]       ) / len(tokens_p["""attention_mask"""]       )	,		)

      snake_case__     :					List[str]    =  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]       )
      snake_case__     :					int    =  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]       )
      self.assertSequenceEqual(tokens_p["""input_ids"""]	,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
      self.assertSequenceEqual(tokens_r["""input_ids"""]	,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )

      self.assertSequenceEqual(
          __SCREAMING_SNAKE_CASE	,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )
      self.assertSequenceEqual(
          __SCREAMING_SNAKE_CASE	,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]       )








"""simple docstring"""

import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
UpperCAmelCase__					=logging.getLogger(__name__)


UpperCAmelCase__					="Hello world! cécé herlolip"


UpperCAmelCase__					=namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)


def lowerCAmelCase_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						BertAbsConfig(
     temp_dir="""."""					, finetune_bert=UpperCamelCase__					, large=UpperCamelCase__					, share_emb=UpperCamelCase__					, use_bert_emb=UpperCamelCase__					, encoder="""bert"""					, max_pos=512					, enc_layers=6					, enc_hidden_size=512					, enc_heads=8					, enc_ff_size=512					, enc_dropout=0.2					, dec_layers=6					, dec_hidden_size=768					, dec_heads=8					, dec_ff_size=2048					, dec_dropout=0.2					, )
 __lowercase						=						torch.load(UpperCamelCase__					, lambda UpperCamelCase__					, UpperCamelCase__			: storage			)
 __lowercase						=						AbsSummarizer(UpperCamelCase__					, torch.device("""cpu"""			)					, UpperCamelCase__			)
 original.eval()

 __lowercase						=						BertAbsSummarizer(UpperCamelCase__					, torch.device("""cpu"""			)			)
 new_model.eval()

 # -------------------
 # Convert the weights
 # -------------------

 logging.info("""convert the model"""			)
 new_model.bert.load_state_dict(original.bert.state_dict()			)
 new_model.decoder.load_state_dict(original.decoder.state_dict()			)
 new_model.generator.load_state_dict(original.generator.state_dict()			)

 # ----------------------------------
 # Make sure the outpus are identical
 # ----------------------------------

 logging.info("""Make sure that the models' outputs are identical"""			)
 __lowercase						=						BertTokenizer.from_pretrained("""bert-base-uncased"""			)

 # prepare the model inputs
 __lowercase						=						tokenizer.encode("""This is sample éàalj'-."""			)
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase__			))			)
 __lowercase						=						torch.tensor(UpperCamelCase__			).unsqueeze(0			)
 __lowercase						=						tokenizer.encode("""This is sample 3 éàalj'-."""			)
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(UpperCamelCase__			))			)
 __lowercase						=						torch.tensor(UpperCamelCase__			).unsqueeze(0			)

 # failsafe to make sure the weights reset does not affect the
 # loaded weights.
 assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight			)			) == 0

 # forward pass
 __lowercase						=						encoder_input_ids
 __lowercase						=						decoder_input_ids
 __lowercase						=						__lowercase						=						None
 __lowercase						=						None
 __lowercase						=						__lowercase						=						None
 __lowercase						=						__lowercase						=						None
 __lowercase						=						None

 # The original model does not apply the geneator layer immediatly but rather in
 # the beam search (where it combines softmax + linear layer). Since we already
 # apply the softmax in our generation process we only apply the linear layer here.
 # We make sure that the outputs of the full stack are identical
 __lowercase						=						original(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						original.generator(UpperCamelCase__			)

 __lowercase						=						new_model(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						new_model.generator(UpperCamelCase__			)

 __lowercase						=						torch.max(torch.abs(output_converted_model - output_original_model			)			).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase__			)			)
 __lowercase						=						torch.max(torch.abs(output_converted_generator - output_original_generator			)			).item()
 print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase__			)			)

 __lowercase						=						torch.allclose(UpperCamelCase__					, UpperCamelCase__					, atol=1E-3			)
 if are_identical:
  logging.info("""all weights are equal up to 1e-3"""			)
 else:
  raise ValueError("""the weights are different. The new model is likely different from the original one."""			)

 # The model has been saved with torch.save(model) and this is bound to the exact
 # directory structure. We save the state_dict instead.
 logging.info("""saving the model's state dictionary"""			)
 torch.save(
     new_model.state_dict()					, """./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""			)


if __name__ == "__main__":
       UpperCAmelCase__					=argparse.ArgumentParser()
       parser.add_argument(
           "--bertabs_checkpoint_path",
           default=None,
           type=str,
           required=True,
           help="Path the official PyTorch dump.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default=None,
           type=str,
           required=True,
           help="Path to the output PyTorch model.",
       )
       UpperCAmelCase__					=parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )
