




'''simple docstring'''





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase_						:      List[Any]       =						logging.get_logger(__name__)

UpperCamelCase_						:      str       =						{
    '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/config.json''',
    # See all BART models at https://huggingface.co/models?filter=bart
}

class        _a    (						lowerCamelCase__							):
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						'bart'
     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						['past_key_values']
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}






     def __init__(			self ,_SCREAMING_SNAKE_CASE=50_265 ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=4_096 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=4_096 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=1_024 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,)							->      Tuple:
          _snake_case							=				vocab_size
          _snake_case							=				max_position_embeddings
          _snake_case							=				d_model
          _snake_case							=				encoder_ffn_dim
          _snake_case							=				encoder_layers
          _snake_case							=				encoder_attention_heads
          _snake_case							=				decoder_ffn_dim
          _snake_case							=				decoder_layers
          _snake_case							=				decoder_attention_heads
          _snake_case							=				dropout
          _snake_case							=				attention_dropout
          _snake_case							=				activation_dropout
          _snake_case							=				activation_function
          _snake_case							=				init_std
          _snake_case							=				encoder_layerdrop
          _snake_case							=				decoder_layerdrop
          _snake_case							=				classifier_dropout
          _snake_case							=				use_cache
          _snake_case							=				encoder_layers
          _snake_case							=				scale_embedding  # scale factor will be sqrt(d_model) if True

          super().__init__(
              num_labels=lowercase__ ,pad_token_id=lowercase__ ,bos_token_id=lowercase__ ,eos_token_id=lowercase__ ,is_encoder_decoder=lowercase__ ,decoder_start_token_id=lowercase__ ,forced_eos_token_id=lowercase__ ,**lowercase__ ,)

          # ensure backward compatibility for BART CNN models
          if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated" ,lowercase__   ):
               _snake_case							=				self.bos_token_id
               warnings.warn(
                   f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                   "The config can simply be saved and uploaded again to be fixed."   )







class        _a    (						lowerCamelCase__							):






     @property
     def     _lowercase     (			self   )							->      List[str]:
          if self.task in ["default", "seq2seq-lm"]:
               _snake_case							=				OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ]   )

               if self.use_past:
                    _snake_case							=				{0: "batch"}
                    _snake_case							=				{0: "batch", 1: "past_decoder_sequence + sequence"}
               else:
                    _snake_case							=				{0: "batch", 1: "decoder_sequence"}
                    _snake_case							=				{0: "batch", 1: "decoder_sequence"}

               if self.use_past:
                    self.fill_with_past_key_values_(lowercase__ ,direction="inputs"   )
          elif self.task == "causal-lm":
               # TODO: figure this case out.
               _snake_case							=				OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ]   )
               if self.use_past:
                    _snake_case							, _snake_case							=				self.num_layers
                    for i in range(lowercase__   ):
                         _snake_case							=				{0: "batch", 2: "past_sequence + sequence"}
                         _snake_case							=				{0: "batch", 2: "past_sequence + sequence"}
          else:
               _snake_case							=				OrderedDict(
                   [
                       ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                       ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                       ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                   ]   )

          return common_inputs






     @property
     def     _lowercase     (			self   )							->      str:
          if self.task in ["default", "seq2seq-lm"]:
               _snake_case							=				super().outputs
          else:
               _snake_case							=				super(lowercase__ ,self   ).outputs
               if self.use_past:
                    _snake_case							, _snake_case							=				self.num_layers
                    for i in range(lowercase__   ):
                         _snake_case							=				{0: "batch", 2: "past_sequence + sequence"}
                         _snake_case							=				{0: "batch", 2: "past_sequence + sequence"}
          return common_outputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,)							->      Union[str, Any]:
          _snake_case							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__   )

          # Generate decoder inputs
          _snake_case							=				seq_length if not self.use_past else 1
          _snake_case							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__   )
          _snake_case							=				{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
          _snake_case							=				dict(**lowercase__ ,**lowercase__   )

          if self.use_past:
               if not is_torch_available():
                    raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
               else:
                    import torch
               _snake_case							, _snake_case							=				common_inputs["input_ids"].shape
               _snake_case							=				common_inputs["decoder_input_ids"].shape[1]
               _snake_case							, _snake_case							=				self.num_attention_heads
               _snake_case							=				(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               _snake_case							=				decoder_seq_length + 3
               _snake_case							=				(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )

               _snake_case							=				torch.cat(
                   [common_inputs["decoder_attention_mask"], torch.ones(lowercase__ ,lowercase__   )] ,dim=1   )

               _snake_case							=				[]
               # If the number of encoder and decoder layers are present in the model configuration, both are considered
               _snake_case							, _snake_case							=				self.num_layers
               _snake_case							=				min(lowercase__ ,lowercase__   )
               _snake_case							=				max(lowercase__ ,lowercase__   ) - min_num_layers
               _snake_case							=				"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

               for _ in range(lowercase__   ):
                    common_inputs["past_key_values"].append(
                        (
                            torch.zeros(lowercase__   ),
                            torch.zeros(lowercase__   ),
                            torch.zeros(lowercase__   ),
                            torch.zeros(lowercase__   ),
                        )   )
               # TODO: test this.
               _snake_case							=				encoder_shape if remaining_side_name == "encoder" else decoder_shape
               for _ in range(lowercase__ ,lowercase__   ):
                    common_inputs["past_key_values"].append((torch.zeros(lowercase__   ), torch.zeros(lowercase__   ))   )
          return common_inputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,)							->      Union[str, Any]:
          _snake_case							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              lowercase__ ,lowercase__ ,lowercase__ ,lowercase__ ,lowercase__   )

          if self.use_past:
               if not is_torch_available():
                    raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
               else:
                    import torch
               _snake_case							, _snake_case							=				common_inputs["input_ids"].shape
               # Not using the same length for past_key_values
               _snake_case							=				seqlen + 2
               _snake_case							, _snake_case							=				self.num_layers
               _snake_case							, _snake_case							=				self.num_attention_heads
               _snake_case							=				(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )

               _snake_case							=				common_inputs["attention_mask"].dtype
               _snake_case							=				torch.cat(
                   [common_inputs["attention_mask"], torch.ones(lowercase__ ,lowercase__ ,dtype=lowercase__   )] ,dim=1   )
               _snake_case							=				[
                   (torch.zeros(lowercase__   ), torch.zeros(lowercase__   )) for _ in range(lowercase__   )
               ]
          return common_inputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,)							->      Optional[Any]:
          # Copied from OnnxConfig.generate_dummy_inputs
          # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
          # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
          _snake_case							=				compute_effective_axis_dimension(
              lowercase__ ,fixed_dimension=OnnxConfig.default_fixed_batch ,num_token_to_add=0   )

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          _snake_case							=				tokenizer.num_special_tokens_to_add(lowercase__   )
          _snake_case							=				compute_effective_axis_dimension(
              lowercase__ ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=lowercase__   )

          # Generate dummy inputs according to compute batch and sequence
          _snake_case							=				[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
          _snake_case							=				dict(tokenizer(lowercase__ ,return_tensors=lowercase__   )   )
          return common_inputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = -1 ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,)							->      Dict:
          if self.task in ["default", "seq2seq-lm"]:
               _snake_case							=				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__   )

          elif self.task == "causal-lm":
               _snake_case							=				self._generate_dummy_inputs_for_causal_lm(
                   lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__   )
          else:
               _snake_case							=				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   lowercase__ ,batch_size=lowercase__ ,seq_length=lowercase__ ,is_pair=lowercase__ ,framework=lowercase__   )

          return common_inputs






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Tuple:
          if self.task in ["default", "seq2seq-lm"]:
               _snake_case							=				super()._flatten_past_key_values_(lowercase__ ,lowercase__ ,lowercase__ ,lowercase__   )
          else:
               _snake_case							=				super(lowercase__ ,self   )._flatten_past_key_values_(
                   lowercase__ ,lowercase__ ,lowercase__ ,lowercase__   )








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				calculate_rouge(_UpperCamelCase ,						_UpperCamelCase ,						**_UpperCamelCase   )
     if save_path is not None:
          save_json(_UpperCamelCase ,						_UpperCamelCase ,						indent=_UpperCamelCase   )
     return metrics  # these print nicely


if __name__ == "__main__":
   fire.Fire(calculate_rouge_path)


