




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
  import os

  import jax.numpy as jnp
  from jax import jit

  from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
  from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

  _snake_case										=       "0.12"  # assumed parallelism: 8


if is_torch_available():
  import torch







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_=None     ):
      if rng is None:
            _A							:     Any			=						random.Random()

      _A							:     int			=						1
      for dim in shape:
            total_dims *= dim

      _A							:     List[Any]			=						[]
      for _ in range(_a     ):
            values.append(rng.randint(0,vocab_size - 1     )     )

      _A							:     Optional[int]			=						np.array(_a,dtype=jnp.intaa     ).reshape(_a     )

      return output







def 				lowerCAmelCase_	(     snake_case_,snake_case_=None     ):
      _A							:     Tuple			=						ids_tensor(_a,vocab_size=2,rng=_a     )
      # make sure that at least one token is attended to for each batch
      _A							:     Tuple			=						1
      return attn_mask







@require_flax
class 						lowercase							:
     _a							 =						None
     _a							 =						()

     def 					a__	(  self				)						->							List[str]:
           _A			,   _A							:     Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()

           # cut to half length & take max batch_size 3
           _A							:     Optional[int]			=						2
           _A							:     Union[str, Any]			=						inputs["""input_ids"""].shape[-1] // 2
           _A							:     str			=						inputs["""input_ids"""][:max_batch_size, :sequence_length]

           _A							:     Optional[int]			=						jnp.ones_like(_a				)
           _A							:     List[str]			=						attention_mask[:max_batch_size, :sequence_length]

           # generate max 5 tokens
           _A							:     List[str]			=						input_ids.shape[-1] + 5
           if config.eos_token_id is not None and config.pad_token_id is None:
                 # hack to allow generate for models such as GPT2 as is done in `generate()`
                 _A							:     Dict			=						config.eos_token_id
           return config, input_ids, attention_mask, max_length

     @is_pt_flax_cross_test
     def 					a__	(  self				)						->							str:
           _A			,   _A			,   _A			,   _A							:     Any			=						self._get_input_ids_and_config()
           _A							:     int			=						False
           _A							:     Union[str, Any]			=						max_length
           _A							:     str			=						0

           for model_class in self.all_generative_model_classes:
                 _A							:     int			=						model_class(_a				)

                 _A							:     Optional[int]			=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 _A							:     str			=						getattr(_a		,     _a				)
                 _A							:     Optional[int]			=						pt_model_class(_a				).eval()
                 _A							:     List[str]			=						load_flax_weights_in_pytorch_model(_a		,     flax_model.params				)

                 _A							:     Any			=						flax_model.generate(_a				).sequences
                 _A							:     Optional[int]			=						pt_model.generate(torch.tensor(_a		,     dtype=torch.long				)				)

                 if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
                       _A							:     int			=						flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

                 self.assertListEqual(pt_generation_outputs.numpy().tolist()		,     flax_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Optional[int]:
           _A			,   _A			,   _A			,   _A							:     Union[str, Any]			=						self._get_input_ids_and_config()
           _A							:     Tuple			=						False
           _A							:     Any			=						max_length

           for model_class in self.all_generative_model_classes:
                 _A							:     List[str]			=						model_class(_a				)

                 _A							:     Optional[Any]			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Tuple			=						jit(model.generate				)
                 _A							:     Optional[Any]			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Dict:
           _A			,   _A			,   _A			,   _A							:     List[str]			=						self._get_input_ids_and_config()
           _A							:     List[Any]			=						True
           _A							:     Optional[Any]			=						max_length

           for model_class in self.all_generative_model_classes:
                 _A							:     int			=						model_class(_a				)

                 _A							:     Union[str, Any]			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     str			=						jit(model.generate				)
                 _A							:     List[str]			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							int:
           _A			,   _A			,   _A			,   _A							:     Any			=						self._get_input_ids_and_config()
           _A							:     Dict			=						False
           _A							:     Tuple			=						max_length
           _A							:     List[Any]			=						2

           for model_class in self.all_generative_model_classes:
                 _A							:     Optional[Any]			=						model_class(_a				)

                 _A							:     Optional[int]			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Union[str, Any]			=						jit(model.generate				)
                 _A							:     Union[str, Any]			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Union[str, Any]:
           _A			,   _A			,   _A			,   _A							:     Any			=						self._get_input_ids_and_config()
           _A							:     str			=						False
           _A							:     List[Any]			=						max_length
           _A							:     int			=						2
           _A							:     Tuple			=						2

           for model_class in self.all_generative_model_classes:
                 _A							:     Any			=						model_class(_a				)

                 _A							:     Any			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[0]		,     input_ids.shape[0] * config.num_return_sequences				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A			,   _A			,   _A			,   _A							:     int			=						self._get_input_ids_and_config()
           _A							:     Union[str, Any]			=						True
           _A							:     List[str]			=						max_length
           _A							:     str			=						0.8
           _A							:     Dict			=						10
           _A							:     List[Any]			=						0.3
           _A							:     str			=						1
           _A							:     Tuple			=						8
           _A							:     Any			=						9

           for model_class in self.all_generative_model_classes:
                 _A							:     Any			=						model_class(_a				)

                 _A							:     List[str]			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Dict			=						jit(model.generate				)
                 _A							:     Any			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Dict:
           _A			,   _A			,   _A			,   _A							:     Any			=						self._get_input_ids_and_config()
           _A							:     Optional[int]			=						max_length
           _A							:     Any			=						1
           _A							:     List[Any]			=						8
           _A							:     Optional[int]			=						9

           for model_class in self.all_generative_model_classes:
                 _A							:     Tuple			=						model_class(_a				)

                 _A							:     int			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Union[str, Any]			=						jit(model.generate				)
                 _A							:     Dict			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Tuple:
           _A			,   _A			,   _A			,   _A							:     List[str]			=						self._get_input_ids_and_config()
           _A							:     str			=						max_length
           _A							:     Optional[int]			=						2
           _A							:     int			=						1
           _A							:     int			=						8
           _A							:     List[Any]			=						9

           for model_class in self.all_generative_model_classes:
                 _A							:     Union[str, Any]			=						model_class(_a				)

                 _A							:     List[Any]			=						model.generate(_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Any			=						jit(model.generate				)
                 _A							:     Tuple			=						jit_generate(_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Dict:
           _A			,   _A			,   _A			,   _A							:     Any			=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _A							:     Optional[Any]			=						attention_mask.at[(0, 0)].set(0				)

           _A							:     str			=						False
           _A							:     int			=						max_length

           for model_class in self.all_generative_model_classes:
                 _A							:     Optional[Any]			=						model_class(_a				)

                 _A							:     Optional[int]			=						model.generate(_a		,     attention_mask=_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Dict			=						jit(model.generate				)
                 _A							:     Optional[Any]			=						jit_generate(_a		,     attention_mask=_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)

     def 					a__	(  self				)						->							Optional[Any]:
           _A			,   _A			,   _A			,   _A							:     int			=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _A							:     List[str]			=						attention_mask.at[(0, 0)].set(0				)

           _A							:     Optional[Any]			=						True
           _A							:     List[Any]			=						max_length

           for model_class in self.all_generative_model_classes:
                 _A							:     Optional[int]			=						model_class(_a				)

                 _A							:     str			=						model.generate(_a		,     attention_mask=_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     Any			=						jit(model.generate				)
                 _A							:     str			=						jit_generate(_a		,     attention_mask=_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)



     def 					a__	(  self				)						->							Optional[int]:
           _A			,   _A			,   _A			,   _A							:     Dict			=						self._get_input_ids_and_config()

           # pad attention mask on the left
           _A							:     Optional[int]			=						attention_mask.at[(0, 0)].set(0				)

           _A							:     Dict			=						2
           _A							:     Optional[Any]			=						max_length

           for model_class in self.all_generative_model_classes:
                 _A							:     Optional[Any]			=						model_class(_a				)

                 _A							:     List[Any]			=						model.generate(_a		,     attention_mask=_a				).sequences
                 self.assertEqual(generation_outputs.shape[-1]		,     _a				)

                 _A							:     List[Any]			=						jit(model.generate				)
                 _A							:     int			=						jit_generate(_a		,     attention_mask=_a				).sequences

                 self.assertListEqual(generation_outputs.tolist()		,     jit_generation_outputs.tolist()				)







@require_flax
class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Optional[Any]:
           _A							:     List[Any]			=						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
           _A							:     Tuple			=						FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

           _A							:     Any			=						"""Hello world"""
           _A							:     Optional[int]			=						tokenizer(_a		,     return_tensors="""np"""				).input_ids

           # typos are quickly detected (the correct argument is `do_sample`)
           with self.assertRaisesRegex(_a		,     """do_samples"""				):
                 model.generate(_a		,     do_samples=_a				)

           # arbitrary arguments that will not be used anywhere are also not accepted
           with self.assertRaisesRegex(_a		,     """foo"""				):
                 _A							:     Optional[Any]			=						{"""foo""": """bar"""}
                 model.generate(_a		,     **_a				)







'''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)
A     =logging.getLogger(__name__)


A     ='Hello world! cécé herlolip'


A     =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 						snake_case_							(_a	:							List[Any]    ,   _a	:							Any     ):
    UpperCAmelCase			       =		BertAbsConfig(
        temp_dir='''.'''    ,   finetune_bert=_a    ,   large=_a    ,   share_emb=_a    ,   use_bert_emb=_a    ,   encoder='''bert'''    ,   max_pos=5_1_2    ,   enc_layers=6    ,   enc_hidden_size=5_1_2    ,   enc_heads=8    ,   enc_ff_size=5_1_2    ,   enc_dropout=0.2    ,   dec_layers=6    ,   dec_hidden_size=7_6_8    ,   dec_heads=8    ,   dec_ff_size=2_0_4_8    ,   dec_dropout=0.2    ,   )
    UpperCAmelCase			       =		torch.load(_a    ,   lambda _a    ,   _a     : storage     )
    UpperCAmelCase			       =		AbsSummarizer(_a    ,   torch.device('''cpu'''     )    ,   _a     )
    original.eval()

    UpperCAmelCase			       =		BertAbsSummarizer(_a    ,   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'''     )
    UpperCAmelCase			       =		BertTokenizer.from_pretrained('''bert-base-uncased'''     )

    # prepare the model inputs
    UpperCAmelCase			       =		tokenizer.encode('''This is sample éàalj\'-.'''     )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).unsqueeze(0     )
    UpperCAmelCase			       =		tokenizer.encode('''This is sample 3 éàalj\'-.'''     )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a     ))     )
    UpperCAmelCase			       =		torch.tensor(_a     ).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
    UpperCAmelCase			       =		encoder_input_ids
    UpperCAmelCase			       =		decoder_input_ids
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		UpperCAmelCase			       =		None
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		original(_a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		original.generator(_a     )

    UpperCAmelCase			       =		new_model(
        _a    ,   _a    ,   _a    ,   _a    ,   _a     )[0]
    UpperCAmelCase			       =		new_model.generator(_a     )

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

    UpperCAmelCase			       =		torch.allclose(_a    ,   _a    ,   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__":
   A     =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.',
   )
   A     =parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




