




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM





@require_torch
class     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Optional[Any]=99    ,       UpperCamelCase_: List[str]=13    ,       UpperCamelCase_: int=16    ,       UpperCamelCase_: Tuple=7    ,       UpperCamelCase_: List[str]=True    ,       UpperCamelCase_: List[str]=True    ,       UpperCamelCase_: Optional[int]=True    ,       UpperCamelCase_: Dict=False    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: Union[str, Any]=2    ,       UpperCamelCase_: Union[str, Any]=32    ,       UpperCamelCase_: Any=4    ,       UpperCamelCase_: Dict=4    ,       UpperCamelCase_: str=30    ,       UpperCamelCase_: List[str]=0    ,       UpperCamelCase_: Dict=1    ,       UpperCamelCase_: Optional[Any]=2    ,       UpperCamelCase_: Dict=None    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						decoder_seq_length
             # For common tests
             __lowerCamelCase   =						self.decoder_seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_attention_mask
             __lowerCamelCase   =						use_labels

             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						d_model
             __lowerCamelCase   =						decoder_layers
             __lowerCamelCase   =						decoder_layers
             __lowerCamelCase   =						decoder_ffn_dim
             __lowerCamelCase   =						decoder_attention_heads
             __lowerCamelCase   =						decoder_attention_heads
             __lowerCamelCase   =						eos_token_id
             __lowerCamelCase   =						bos_token_id
             __lowerCamelCase   =						pad_token_id
             __lowerCamelCase   =						decoder_start_token_id
             __lowerCamelCase   =						use_cache
             __lowerCamelCase   =						max_position_embeddings

             __lowerCamelCase   =						None
             __lowerCamelCase   =						decoder_seq_length
             __lowerCamelCase   =						2
             __lowerCamelCase   =						1
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.decoder_seq_length]    ,       self.vocab_size   )

             __lowerCamelCase   =						None
             if self.use_attention_mask:
                    __lowerCamelCase   =						ids_tensor([self.batch_size, self.decoder_seq_length]    ,       vocab_size=2   )

             __lowerCamelCase   =						None
             if self.use_labels:
                    __lowerCamelCase   =						ids_tensor([self.batch_size, self.decoder_seq_length]    ,       self.vocab_size   )

             __lowerCamelCase   =						TrOCRConfig(
                 vocab_size=self.vocab_size    ,       d_model=self.d_model    ,       decoder_layers=self.decoder_layers    ,       decoder_ffn_dim=self.decoder_ffn_dim    ,       decoder_attention_heads=self.decoder_attention_heads    ,       eos_token_id=self.eos_token_id    ,       bos_token_id=self.bos_token_id    ,       use_cache=self.use_cache    ,       pad_token_id=self.pad_token_id    ,       decoder_start_token_id=self.decoder_start_token_id    ,       max_position_embeddings=self.max_position_embeddings    ,       )

             return (config, input_ids, attention_mask, lm_labels)
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: Optional[Any]    ,       UpperCamelCase_: Dict    ,       ):
             __lowerCamelCase   =						True
             __lowerCamelCase   =						TrOCRDecoder(config=UpperCamelCase_   ).to(UpperCamelCase_   ).eval()
             __lowerCamelCase   =						input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             __lowerCamelCase   =						model(UpperCamelCase_    ,       use_cache=UpperCamelCase_   )
             __lowerCamelCase   =						model(UpperCamelCase_   )
             __lowerCamelCase   =						model(UpperCamelCase_    ,       use_cache=UpperCamelCase_   )

             self.parent.assertTrue(len(UpperCamelCase_   ) == len(UpperCamelCase_   )   )
             self.parent.assertTrue(len(UpperCamelCase_   ) == len(UpperCamelCase_   ) + 1   )

             __lowerCamelCase   =						outputs["""past_key_values"""]

             # create hypothetical next token and extent to next_input_ids
             __lowerCamelCase   =						ids_tensor((2, 1)    ,       config.vocab_size - 1   ) + 1

             # append to next input_ids and
             __lowerCamelCase   =						torch.cat([input_ids, next_tokens]    ,       dim=-1   )

             __lowerCamelCase   =						model(UpperCamelCase_   )["""last_hidden_state"""]
             __lowerCamelCase   =						model(UpperCamelCase_    ,       past_key_values=UpperCamelCase_   )["""last_hidden_state"""]

             # select random slice
             __lowerCamelCase   =						ids_tensor((1,)    ,       output_from_past.shape[-1]   ).item()
             __lowerCamelCase   =						output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             __lowerCamelCase   =						output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             assert torch.allclose(UpperCamelCase_    ,       UpperCamelCase_    ,       atol=1E-3   )




      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.prepare_config_and_inputs()
             __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						config_and_inputs

             __lowerCamelCase   =						{"""input_ids""": input_ids, """attention_mask""": attention_mask}
             return config, inputs_dict





@require_torch
class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Tuple    		=   (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      UpperCAmelCase__      :	Dict    		=   (TrOCRForCausalLM,) if is_torch_available() else ()
      UpperCAmelCase__      :	Union[str, Any]    		=   {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
      UpperCAmelCase__      :	Union[str, Any]    		=   True
      UpperCAmelCase__      :	Dict    		=   False
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						TrOCRStandaloneDecoderModelTester(self    ,       is_training=UpperCamelCase_   )
             __lowerCamelCase   =						ConfigTester(self    ,       config_class=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             pass
      def 			lowerCAmelCase__						(				self: List[str]   ):
             pass
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass
      def 			lowerCAmelCase__						(				self: List[str]   ):
             self.config_tester.run_common_tests()
      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             return




      @unittest.skip("""The model doesn't support left padding"""   )  # and it's not used enough to be worth fixing :)
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             pass


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   :     Optional[int]       =				logging.getLogger(__name__)


A   :     int       =				"Hello world! cécé herlolip"


A   :     List[Any]       =				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    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     BertAbsConfig(
          temp_dir="."   ,					finetune_bert=__UpperCamelCase   ,					large=__UpperCamelCase   ,					share_emb=__UpperCamelCase   ,					use_bert_emb=__UpperCamelCase   ,					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   ,					)
      SCREAMING_SNAKE_CASE_      =     torch.load(__UpperCamelCase   ,					lambda __UpperCamelCase   ,					__UpperCamelCase : storage )
      SCREAMING_SNAKE_CASE_      =     AbsSummarizer(__UpperCamelCase   ,					torch.device("cpu" )   ,					__UpperCamelCase )
      original.eval()

      SCREAMING_SNAKE_CASE_      =     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" )
      SCREAMING_SNAKE_CASE_      =     BertTokenizer.from_pretrained("bert-base-uncased" )

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

      SCREAMING_SNAKE_CASE_      =     new_model(
          __UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase )[0]
      SCREAMING_SNAKE_CASE_      =     new_model.generator(__UpperCamelCase )

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

      SCREAMING_SNAKE_CASE_      =     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__":
      A   :     Any       =				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   :     int       =				parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )


