"""simple docstring"""


import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
__UpperCamelCase								=    logging.getLogger(__name__)

__UpperCamelCase								=    int(1_0000)  # Hardcoded max length to avoid infinite loop

__UpperCamelCase								=    {
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
__UpperCamelCase								=    '''In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>'''







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   List[Any]:
     np.random.seed(args.seed      )
     torch.manual_seed(args.seed      )
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed      )







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Union[str, Any]:
     if args.temperature > 0.7:
          logger.info('CTRL typically works better with lower temperatures (and lower top_k).'      )

     snake_case_       =      tokenizer.encode(UpperCAmelCase		,      add_special_tokens=UpperCAmelCase      )
     if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()      ):
          logger.info('WARNING! You are not starting your generation from a control code so you won\'t get good results'      )
     return prompt_text







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Tuple:
     # kwargs = {"language": None, "mask_token_id": None}

     # Set the language
     snake_case_       =      hasattr(model.config		,      'use_lang_emb'      ) and model.config.use_lang_emb
     if hasattr(model.config		,      'lang2id'      ) and use_lang_emb:
          snake_case_       =      model.config.langaid.keys()
          if args.xlm_language in available_languages:
               snake_case_       =      args.xlm_language
          else:
               snake_case_       =      None
               while language not in available_languages:
                    snake_case_       =      input('Using XLM. Select language in ' + str(list(UpperCAmelCase      )      ) + ' >>> '      )

          snake_case_       =      model.config.langaid[language]
          # kwargs["language"] = tokenizer.lang2id[language]

     # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
     # XLM masked-language modeling (MLM) models need masked token
     # is_xlm_mlm = "mlm" in args.model_name_or_path
     # if is_xlm_mlm:
     #     kwargs["mask_token_id"] = tokenizer.mask_token_id

     return prompt_text







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   int:
     snake_case_       =      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     snake_case_       =      prefix + prompt_text
     return prompt_text







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Tuple:
     snake_case_       =      args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
     snake_case_       =      prefix + prompt_text
     return prompt_text


__UpperCamelCase								=    {
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[Any]:
     if length < 0 and max_sequence_length > 0:
          snake_case_       =      max_sequence_length
     elif 0 < max_sequence_length < length:
          snake_case_       =      max_sequence_length  # No generation bigger than model size
     elif length < 0:
          snake_case_       =      MAX_LENGTH  # avoid infinite loop
     return length







def       UpperCAmelCase					(	UpperCAmelCase      )       ->   Union[str, Any]:
     snake_case_       =      None
     if hasattr(UpperCAmelCase		,      'hidden_size'      ):
          snake_case_       =      model_config.hidden_size
     elif hasattr(UpperCAmelCase		,      'n_embed'      ):
          snake_case_       =      model_config.n_embed
     elif hasattr(UpperCAmelCase		,      'n_embd'      ):
          snake_case_       =      model_config.n_embd

     snake_case_       =      None
     if hasattr(UpperCAmelCase		,      'num_attention_heads'      ):
          snake_case_       =      model_config.num_attention_heads
     elif hasattr(UpperCAmelCase		,      'n_head'      ):
          snake_case_       =      model_config.n_head

     if embedding_size is None or num_head is None or num_head == 0:
          raise ValueError('Check the model config'      )

     snake_case_       =      int(embedding_size / num_head      )
     if hasattr(UpperCAmelCase		,      'n_layer'      ):
          snake_case_       =      model_config.n_layer
     elif hasattr(UpperCAmelCase		,      'num_hidden_layers'      ):
          snake_case_       =      model_config.num_hidden_layers
     else:
          raise ValueError('Number of hidden layers couldn\'t be determined from the model config'      )

     return num_layer, num_head, num_embedding_size_per_head







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   List[str]:
     snake_case_ ,			snake_case_ ,			snake_case_       =      sparse_model_config(model.config      )
     if model.config.model_type == "bloom":
          snake_case_       =      tuple(
              (
                  torch.empty(int(num_attention_heads * batch_size      )		,      UpperCAmelCase		,      UpperCAmelCase      )
                  .to(model.dtype      )
                  .to(model.device      ),
                  torch.empty(int(num_attention_heads * batch_size      )		,      UpperCAmelCase		,      UpperCAmelCase      )
                  .to(model.dtype      )
                  .to(model.device      ),
              )
              for _ in range(UpperCAmelCase      )      )
     else:
          snake_case_       =      tuple(
              (
                  torch.empty(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
                  .to(model.dtype      )
                  .to(model.device      ),
                  torch.empty(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
                  .to(model.dtype      )
                  .to(model.device      ),
              )
              for _ in range(UpperCAmelCase      )      )
     return past_key_values







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   Tuple:
     snake_case_       =      len(UpperCAmelCase      )
     snake_case_       =      tokenizer.batch_encode_plus(UpperCAmelCase		,      return_tensors='pt'      )
     snake_case_       =      dummy_input.to(model.device      )
     if model.config.use_cache:
          snake_case_       =      generate_past_key_values(UpperCAmelCase		,      UpperCAmelCase		,      1      )
     snake_case_       =      torch.cat(
         [
             torch.zeros(dummy_input['attention_mask'].shape[0]		,      1      )
             .to(dummy_input['attention_mask'].dtype      )
             .to(model.device      ),
             dummy_input['attention_mask'],
         ]		,      -1		,      )
     return dummy_input




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   ("_optimized", "_default")







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Union[str, Any]:
            snake_case_       =      optimized
            snake_case_       =      default







       def __call__(				self,							*lowerCAmelCase__,							**lowerCAmelCase__)   ->   Any:
            if kwargs["past_key_values"] is None and self._default.config.use_cache:
                 snake_case_       =      generate_past_key_values(self._default,							kwargs['input_ids'].shape[0],							0)
            kwargs.pop('position_ids',							lowerCAmelCase__)
            for k in list(kwargs.keys()):
                 if kwargs[k] is None or isinstance(kwargs[k],							lowerCAmelCase__):
                      kwargs.pop(lowerCAmelCase__)
            snake_case_       =      self._optimized(**lowerCAmelCase__)
            snake_case_       =      outputs[0]
            snake_case_       =      outputs[1]
            snake_case_       =      CausalLMOutputWithPast(
                loss=lowerCAmelCase__,							logits=lowerCAmelCase__,							past_key_values=lowerCAmelCase__,							hidden_states=lowerCAmelCase__,							attentions=lowerCAmelCase__,							)
            return fixed_output







       def __getattr__(				self,							lowerCAmelCase__)   ->   List[str]:
            return getattr(self._default,							lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            return self._default.prepare_inputs_for_generation(
                lowerCAmelCase__,							past_key_values=lowerCAmelCase__,							inputs_embeds=lowerCAmelCase__,							use_cache=lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Tuple[Tuple[torch.Tensor]]:
            return self._default._reorder_cache(lowerCAmelCase__,							lowerCAmelCase__)







def       UpperCAmelCase					(	)       ->   Tuple:
     snake_case_       =      argparse.ArgumentParser()
     parser.add_argument(
         '--model_type'		,      default=UpperCAmelCase		,      type=UpperCAmelCase		,      required=UpperCAmelCase		,      help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()      )		,      )
     parser.add_argument(
         '--model_name_or_path'		,      default=UpperCAmelCase		,      type=UpperCAmelCase		,      required=UpperCAmelCase		,      help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()      )		,      )

     parser.add_argument('--prompt'		,      type=UpperCAmelCase		,      default=''      )
     parser.add_argument('--length'		,      type=UpperCAmelCase		,      default=20      )
     parser.add_argument('--stop_token'		,      type=UpperCAmelCase		,      default=UpperCAmelCase		,      help='Token at which text generation is stopped'      )

     parser.add_argument(
         '--temperature'		,      type=UpperCAmelCase		,      default=1.0		,      help='temperature of 1.0 has no effect, lower tend toward greedy sampling'		,      )
     parser.add_argument(
         '--repetition_penalty'		,      type=UpperCAmelCase		,      default=1.0		,      help='primarily useful for CTRL model; in that case, use 1.2'      )
     parser.add_argument('--k'		,      type=UpperCAmelCase		,      default=0      )
     parser.add_argument('--p'		,      type=UpperCAmelCase		,      default=0.9      )

     parser.add_argument('--prefix'		,      type=UpperCAmelCase		,      default=''		,      help='Text added prior to input.'      )
     parser.add_argument('--padding_text'		,      type=UpperCAmelCase		,      default=''		,      help='Deprecated, the use of `--prefix` is preferred.'      )
     parser.add_argument('--xlm_language'		,      type=UpperCAmelCase		,      default=''		,      help='Optional language when used with the XLM model.'      )

     parser.add_argument('--seed'		,      type=UpperCAmelCase		,      default=42		,      help='random seed for initialization'      )
     parser.add_argument('--no_cuda'		,      action='store_true'		,      help='Avoid using CUDA when available'      )
     parser.add_argument('--num_return_sequences'		,      type=UpperCAmelCase		,      default=1		,      help='The number of samples to generate.'      )
     parser.add_argument(
         '--fp16'		,      action='store_true'		,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'		,      )
     parser.add_argument('--jit'		,      action='store_true'		,      help='Whether or not to use jit trace to accelerate inference'      )
     snake_case_       =      parser.parse_args()

     snake_case_       =      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
     snake_case_       =      0 if args.no_cuda else torch.cuda.device_count()

     logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'      )

     set_seed(UpperCAmelCase      )

     # Initialize the model and tokenizer
     try:
          snake_case_       =      args.model_type.lower()
          snake_case_ ,			snake_case_       =      MODEL_CLASSES[args.model_type]
     except KeyError:
          raise KeyError('the model {} you specified is not supported. You are welcome to add it and open a PR :)'      )

     snake_case_       =      tokenizer_class.from_pretrained(args.model_name_or_path      )
     if tokenizer.pad_token is None:
          snake_case_       =      tokenizer.eos_token
     snake_case_       =      model_class.from_pretrained(args.model_name_or_path      )
     model.to(args.device      )

     if args.fpaa:
          model.half()
     snake_case_       =      getattr(model.config		,      'max_position_embeddings'		,      0      )
     snake_case_       =      adjust_length_to_model(args.length		,      max_sequence_length=UpperCAmelCase      )
     logger.info(UpperCAmelCase      )

     snake_case_       =      args.prompt if args.prompt else input('Model prompt >>> '      )

     # Different models need different input formatting and/or extra arguments
     snake_case_       =      args.model_type in PREPROCESSING_FUNCTIONS.keys()
     if requires_preprocessing:
          snake_case_       =      PREPROCESSING_FUNCTIONS.get(args.model_type      )
          snake_case_       =      prepare_input(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )

          if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
               snake_case_       =      {'add_space_before_punct_symbol': True}
          else:
               snake_case_       =      {}

          snake_case_       =      tokenizer.encode(
              UpperCAmelCase		,      add_special_tokens=UpperCAmelCase		,      return_tensors='pt'		,      **UpperCAmelCase      )
     else:
          snake_case_       =      args.prefix if args.prefix else args.padding_text
          snake_case_       =      tokenizer.encode(prefix + prompt_text		,      add_special_tokens=UpperCAmelCase		,      return_tensors='pt'      )
     snake_case_       =      encoded_prompt.to(args.device      )

     if encoded_prompt.size()[-1] == 0:
          snake_case_       =      None
     else:
          snake_case_       =      encoded_prompt

     if args.jit:
          snake_case_       =      ['enable jit']
          snake_case_       =      prepare_jit_inputs(UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )
          torch._C._jit_set_texpr_fuser_enabled(UpperCAmelCase      )
          snake_case_       =      False
          if hasattr(UpperCAmelCase		,      'forward'      ):
               snake_case_       =      inspect.signature(model.forward      )
          else:
               snake_case_       =      inspect.signature(model.__call__      )
          snake_case_       =      tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCAmelCase		,      UpperCAmelCase      ) is not None      )
          snake_case_       =      torch.jit.trace(UpperCAmelCase		,      UpperCAmelCase		,      strict=UpperCAmelCase      )
          snake_case_       =      torch.jit.freeze(traced_model.eval()      )
          traced_model(*UpperCAmelCase      )
          traced_model(*UpperCAmelCase      )

          snake_case_       =      _ModelFallbackWrapper(UpperCAmelCase		,      UpperCAmelCase      )

     snake_case_       =      model.generate(
         input_ids=UpperCAmelCase		,      max_length=args.length + len(encoded_prompt[0]      )		,      temperature=args.temperature		,      top_k=args.k		,      top_p=args.p		,      repetition_penalty=args.repetition_penalty		,      do_sample=UpperCAmelCase		,      num_return_sequences=args.num_return_sequences		,      )

     # Remove the batch dimension when returning multiple sequences
     if len(output_sequences.shape      ) > 2:
          output_sequences.squeeze_()

     snake_case_       =      []

     for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase      ):
          print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='      )
          snake_case_       =      generated_sequence.tolist()

          # Decode text
          snake_case_       =      tokenizer.decode(UpperCAmelCase		,      clean_up_tokenization_spaces=UpperCAmelCase      )

          # Remove all text after the stop token
          snake_case_       =      text[: text.find(args.stop_token      ) if args.stop_token else None]

          # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
          snake_case_       =      (
              prompt_text + text[len(tokenizer.decode(encoded_prompt[0]		,      clean_up_tokenization_spaces=UpperCAmelCase      )      ) :]
          )

          generated_sequences.append(UpperCAmelCase      )
          print(UpperCAmelCase      )

     return generated_sequences


if __name__ == "__main__":
    main()


"""simple docstring"""


import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def       UpperCAmelCase					(	)       ->   str:
     def filter_log(UpperCAmelCase      ):
          return False if "This IS expected if you are initializing" in record.msg else True

     snake_case_       =      datasets.utils.logging.get_logger('transformers.modeling_utils'      )
     logger.addFilter(UpperCAmelCase      )
     try:
          yield
     finally:
          logger.removeFilter(UpperCAmelCase      )


__UpperCamelCase								=    '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

__UpperCamelCase								=    '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

__UpperCamelCase								=    '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,	_KWARGS_DESCRIPTION     )
class 				UpperCamelCase      (       datasets.Metric     ):







       def 	a_		(				self)   ->   Dict:
            return datasets.MetricInfo(
                description=_DESCRIPTION,							citation=_CITATION,							homepage='https://github.com/Tiiiger/bert_score',							inputs_description=_KWARGS_DESCRIPTION,							features=datasets.Features(
                    {
                        'predictions': datasets.Value('string',							id='sequence'),
                        'references': datasets.Sequence(datasets.Value('string',							id='sequence'),							id='references'),
                    }),							codebase_urls=['https://github.com/Tiiiger/bert_score'],							reference_urls=[
                    'https://github.com/Tiiiger/bert_score',
                    'https://arxiv.org/abs/1904.09675',
                ],							)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=64,							lowerCAmelCase__=4,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=None,							lowerCAmelCase__=False,							)   ->   Union[str, Any]:
            snake_case_       =      bert_score.utils.get_hash
            snake_case_       =      bert_score.BERTScorer

            if version.parse(bert_score.__version__) >= version.parse('0.3.10'):
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
                 snake_case_       =      functools.partial(lowerCAmelCase__,							use_fast_tokenizer=lowerCAmelCase__)
            elif use_fast_tokenizer:
                 raise ImportWarning(
                     'To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'
                     'You can install it with `pip install "bert-score>=0.3.10"`.')

            if model_type is None:
                 assert lang is not None, "either lang or model_type should be specified"
                 snake_case_       =      bert_score.utils.langamodel[lang.lower()]

            if num_layers is None:
                 snake_case_       =      bert_score.utils.modelalayers[model_type]

            snake_case_       =      get_hash(
                model=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							use_custom_baseline=baseline_path is not None,							)

            with filter_logging_context():
                 if not hasattr(self,							'cached_bertscorer') or self.cached_bertscorer.hash != hashcode:
                      snake_case_       =      scorer(
                          model_type=lowerCAmelCase__,							num_layers=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							nthreads=lowerCAmelCase__,							all_layers=lowerCAmelCase__,							idf=lowerCAmelCase__,							device=lowerCAmelCase__,							lang=lowerCAmelCase__,							rescale_with_baseline=lowerCAmelCase__,							baseline_path=lowerCAmelCase__,							)

            ((snake_case_) ,			(snake_case_) ,			(snake_case_))       =      self.cached_bertscorer.score(
                cands=lowerCAmelCase__,							refs=lowerCAmelCase__,							verbose=lowerCAmelCase__,							batch_size=lowerCAmelCase__,							)
            snake_case_       =      {
                'precision': P.tolist(),
                'recall': R.tolist(),
                'f1': F.tolist(),
                'hashcode': hashcode,
            }
            return output_dict







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if references is not None:
                 snake_case_       =      [[ref] if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else ref for ref in references]
            super().add_batch(predictions=lowerCAmelCase__,							references=lowerCAmelCase__,							**lowerCAmelCase__)







       def 	a_		(				self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)   ->   List[str]:
            # References can be strings or lists of strings
            # Let's change strings to lists of strings with one element
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_       =      [reference]
            super().add(prediction=lowerCAmelCase__,							reference=lowerCAmelCase__,							**lowerCAmelCase__)

