




"""simple docstring"""

import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
    import tensorflow as tf

    tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset


_UpperCamelCase		    =						5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[int]:
 return PTtoTFCommand(
     args.model_name				,				args.local_dir				,				args.max_error				,				args.new_weights				,				args.no_pr				,				args.push				,				args.extra_commit_description				,				args.override_model_class				,				)






class  __a				(	__magic_name__					):





       """simple docstring"""


       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parser.add_parser(
            "pt-to-tf" ,				help=(
                "CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."
                " Can also be used to validate existing weights without opening PRs, with --no-pr."
            ) ,				)
        train_parser.add_argument(
            "--model-name" ,				type=snake_case ,				required=snake_case ,				help="The model name, including owner/organization, as seen on the hub." ,				)
        train_parser.add_argument(
            "--local-dir" ,				type=snake_case ,				default="" ,				help="Optional local directory of the model repository. Defaults to /tmp/{model_name}" ,				)
        train_parser.add_argument(
            "--max-error" ,				type=snake_case ,				default=snake_case ,				help=(
                F"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
            ) ,				)
        train_parser.add_argument(
            "--new-weights" ,				action="store_true" ,				help="Optional flag to create new TensorFlow weights, even if they already exist." ,				)
        train_parser.add_argument(
            "--no-pr" ,				action="store_true" ,				help="Optional flag to NOT open a PR with converted weights."      )
        train_parser.add_argument(
            "--push" ,				action="store_true" ,				help="Optional flag to push the weights directly to `main` (requires permissions)" ,				)
        train_parser.add_argument(
            "--extra-commit-description" ,				type=snake_case ,				default="" ,				help="Optional additional commit description to use when opening a PR (e.g. to tag the owner)." ,				)
        train_parser.add_argument(
            "--override-model-class" ,				type=snake_case ,				default=snake_case ,				help="If you think you know better than the auto-detector, you can specify the model class here. "
            "Can be either an AutoModel class or a specific model class like BertForSequenceClassification." ,				)
        train_parser.set_defaults(func=snake_case      )






       @staticmethod
       def  SCREAMING_SNAKE_CASE_  (      snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							set(pt_outputs.keys()      )
        lowerCAmelCase__      :							Tuple    =							set(tf_outputs.keys()      )
        if pt_out_attrs != tf_out_attrs:
         raise ValueError(
             F"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
             F""" {tf_out_attrs})"""      )

        # 2. For each output attribute, computes the difference
        def _find_pt_tf_differences(snake_case ,				snake_case ,				snake_case ,				snake_case=""      ):
         # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
         # recursivelly, keeping the name of the attribute.
         if isinstance(snake_case ,				torch.Tensor      ):
          lowerCAmelCase__      :							Optional[int]    =							np.max(np.abs(pt_out.numpy() - tf_out.numpy()      )      )
          lowerCAmelCase__      :							Optional[Any]    =							tensor_difference
         else:
          lowerCAmelCase__      :							Any    =							attr_name
          for i, pt_item in enumerate(snake_case      ):
           # If it is a named attribute, we keep the name. Otherwise, just its index.
           if isinstance(snake_case ,				snake_case      ):
            lowerCAmelCase__      :							List[Any]    =							root_name + pt_item
            lowerCAmelCase__      :							Optional[int]    =							tf_out[pt_item]
            lowerCAmelCase__      :							Dict    =							pt_out[pt_item]
           else:
            lowerCAmelCase__      :							Any    =							root_name + F"""[{i}]"""
            lowerCAmelCase__      :							Tuple    =							tf_out[i]
           lowerCAmelCase__      :							Union[str, Any]    =							_find_pt_tf_differences(snake_case ,				snake_case ,				snake_case ,				snake_case      )

         return differences

        return _find_pt_tf_differences(snake_case ,				snake_case ,				{}      )






       def __init__(      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				*snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							logging.get_logger("transformers-cli/pt_to_tf"      )
        lowerCAmelCase__      :							Optional[int]    =							model_name
        lowerCAmelCase__      :							List[str]    =							local_dir if local_dir else os.path.join("/tmp" ,				snake_case      )
        lowerCAmelCase__      :							str    =							max_error
        lowerCAmelCase__      :							Any    =							new_weights
        lowerCAmelCase__      :							int    =							no_pr
        lowerCAmelCase__      :							Any    =							push
        lowerCAmelCase__      :							Dict    =							extra_commit_description
        lowerCAmelCase__      :							Union[str, Any]    =							override_model_class






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        def _get_audio_input():
         lowerCAmelCase__      :							str    =							load_dataset("hf-internal-testing/librispeech_asr_dummy" ,				"clean" ,				split="validation"      )
         lowerCAmelCase__      :							Any    =							ds.sort("id"      ).select(range(2      )      )[:2]["audio"]
         lowerCAmelCase__      :							Any    =							[x["array"] for x in speech_samples]
         return raw_samples

        lowerCAmelCase__      :							Optional[int]    =							type(pt_model.config      )
        if model_config_class in PROCESSOR_MAPPING:
         lowerCAmelCase__      :							str    =							AutoProcessor.from_pretrained(self._local_dir      )
         if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
          lowerCAmelCase__      :							List[Any]    =							processor.tokenizer.eos_token
        elif model_config_class in IMAGE_PROCESSOR_MAPPING:
         lowerCAmelCase__      :							Union[str, Any]    =							AutoImageProcessor.from_pretrained(self._local_dir      )
        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
         lowerCAmelCase__      :							Optional[Any]    =							AutoFeatureExtractor.from_pretrained(self._local_dir      )
        elif model_config_class in TOKENIZER_MAPPING:
         lowerCAmelCase__      :							List[str]    =							AutoTokenizer.from_pretrained(self._local_dir      )
         if processor.pad_token is None:
          lowerCAmelCase__      :							List[Any]    =							processor.eos_token
        else:
         raise ValueError(F"""Unknown data processing type (model config type: {model_config_class})"""      )

        lowerCAmelCase__      :							Any    =							set(inspect.signature(pt_model.forward      ).parameters.keys()      )
        lowerCAmelCase__      :							Optional[int]    =							{}
        if "input_ids" in model_forward_signature:
         processor_inputs.update(
             {
                 "text": ["Hi there!", "I am a batch with more than one row and different input lengths."],
                 "padding": True,
                 "truncation": True,
             }      )
        if "pixel_values" in model_forward_signature:
         lowerCAmelCase__      :							List[Any]    =							load_dataset("cifar10" ,				"plain_text" ,				split="test"      )[:2]["img"]
         processor_inputs.update({"images": sample_images}      )
        if "input_features" in model_forward_signature:
         lowerCAmelCase__      :							List[str]    =							inspect.signature(processor.feature_extractor      ).parameters
         # Pad to the largest input length by default but take feature extractor default
         # padding value if it exists e.g. "max_length" and is not False or None
         if "padding" in feature_extractor_signature:
          lowerCAmelCase__      :							str    =							feature_extractor_signature["padding"].default
          if default_strategy is not False and default_strategy is not None:
           lowerCAmelCase__      :							Tuple    =							default_strategy
          else:
           lowerCAmelCase__      :							Any    =							True
         else:
          lowerCAmelCase__      :							Any    =							True
         processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy}      )
        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
         processor_inputs.update({"audio": _get_audio_input(), "padding": True}      )
        lowerCAmelCase__      :							Optional[int]    =							processor(**snake_case ,				return_tensors="pt"      )
        lowerCAmelCase__      :							Dict    =							processor(**snake_case ,				return_tensors="tf"      )

        # Extra input requirements, in addition to the input modality
        if (
            config.is_encoder_decoder
            or (hasattr(snake_case ,				"encoder"      ) and hasattr(snake_case ,				"decoder"      ))
            or "decoder_input_ids" in tf_dummy_inputs
        ):
         lowerCAmelCase__      :							Any    =							np.asarray([[1], [1]] ,				dtype=snake_case      ) * (pt_model.config.decoder_start_token_id or 0)
         pt_input.update({"decoder_input_ids": torch.tensor(snake_case      )}      )
         tf_input.update({"decoder_input_ids": tf.convert_to_tensor(snake_case      )}      )

        return pt_input, tf_input






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        if version.parse(huggingface_hub.__version__      ) < version.parse("0.9.0"      ):
         raise ImportError(
             "The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"
             " installation."      )
        else:
         from huggingface_hub import Repository, create_commit
         from huggingface_hub._commit_api import CommitOperationAdd

        # Fetch remote data
        lowerCAmelCase__      :							Tuple    =							Repository(local_dir=self._local_dir ,				clone_from=self._model_name      )

        # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
        lowerCAmelCase__      :							List[Any]    =							AutoConfig.from_pretrained(self._local_dir      )
        lowerCAmelCase__      :							Optional[Any]    =							config.architectures
        if self._override_model_class is not None:
         if self._override_model_class.startswith("TF"      ):
          lowerCAmelCase__      :							List[str]    =							[self._override_model_class[2:]]
         else:
          lowerCAmelCase__      :							Dict    =							[self._override_model_class]
         try:
          lowerCAmelCase__      :							Optional[Any]    =							getattr(import_module("transformers"      ) ,				architectures[0]      )
         except AttributeError:
          raise ValueError(F"""Model class {self._override_model_class} not found in transformers."""      )
         try:
          lowerCAmelCase__      :							List[str]    =							getattr(import_module("transformers"      ) ,				"TF" + architectures[0]      )
         except AttributeError:
          raise ValueError(F"""TF model class TF{self._override_model_class} not found in transformers."""      )
        elif architectures is None:  # No architecture defined -- use auto classes
         lowerCAmelCase__      :							Dict    =							getattr(import_module("transformers"      ) ,				"AutoModel"      )
         lowerCAmelCase__      :							Tuple    =							getattr(import_module("transformers"      ) ,				"TFAutoModel"      )
         self._logger.warning("No detected architecture, using AutoModel/TFAutoModel"      )
        else:  # Architecture defined -- use it
         if len(snake_case      ) > 1:
          raise ValueError(F"""More than one architecture was found, aborting. (architectures = {architectures})"""      )
         self._logger.warning(F"""Detected architecture: {architectures[0]}"""      )
         lowerCAmelCase__      :							Tuple    =							getattr(import_module("transformers"      ) ,				architectures[0]      )
         try:
          lowerCAmelCase__      :							int    =							getattr(import_module("transformers"      ) ,				"TF" + architectures[0]      )
         except AttributeError:
          raise AttributeError(F"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""      )

        # Check the TF dummy inputs to see what keys we need in the forward pass
        lowerCAmelCase__      :							Any    =							tf_class.from_config(snake_case      )
        lowerCAmelCase__      :							Union[str, Any]    =							tf_from_pt_model.dummy_inputs

        del tf_from_pt_model  # Try to keep only one model in memory at a time

        # Load the model and get some basic inputs
        lowerCAmelCase__      :							int    =							pt_class.from_pretrained(self._local_dir      )
        pt_model.eval()

        lowerCAmelCase__      :							Dict    =							self.get_inputs(snake_case ,				snake_case ,				snake_case      )

        with torch.no_grad():
         lowerCAmelCase__      :							Optional[int]    =							pt_model(**snake_case ,				output_hidden_states=snake_case      )
        del pt_model  # will no longer be used, and may have a large memory footprint

        lowerCAmelCase__      :							str    =							tf_class.from_pretrained(self._local_dir ,				from_pt=snake_case      )
        lowerCAmelCase__      :							Optional[int]    =							tf_from_pt_model(**snake_case ,				output_hidden_states=snake_case ,				training=snake_case      )

        # Confirms that cross loading PT weights into TF worked.
        lowerCAmelCase__      :							str    =							self.find_pt_tf_differences(snake_case ,				snake_case      )
        lowerCAmelCase__      :							List[str]    =							{k: v for k, v in crossload_differences.items() if "hidden" not in k}
        lowerCAmelCase__      :							Any    =							{k: v for k, v in crossload_differences.items() if "hidden" in k}
        if len(snake_case      ) == 0 and architectures is not None:
         raise ValueError(
             F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
             " output was found. All outputs start with 'hidden'"      )
        lowerCAmelCase__      :							Optional[int]    =							max(output_differences.values()      ) if output_differences else 0.0
        lowerCAmelCase__      :							Dict    =							max(hidden_differences.values()      )
        if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
         raise ValueError(
             "The cross-loaded TensorFlow model has different outputs, something went wrong!\n"
             + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]      )
             + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]      )      )

        # Save the weights in a TF format (if needed) and confirms that the results are still good
        lowerCAmelCase__      :							Union[str, Any]    =							os.path.join(self._local_dir ,				snake_case      )
        lowerCAmelCase__      :							List[Any]    =							os.path.join(self._local_dir ,				snake_case      )
        if (not os.path.exists(snake_case      ) and not os.path.exists(snake_case      )) or self._new_weights:
         tf_from_pt_model.save_pretrained(self._local_dir      )
        del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

        lowerCAmelCase__      :							Tuple    =							tf_class.from_pretrained(self._local_dir      )
        lowerCAmelCase__      :							List[str]    =							tf_model(**snake_case ,				output_hidden_states=snake_case      )

        lowerCAmelCase__      :							List[Any]    =							self.find_pt_tf_differences(snake_case ,				snake_case      )
        lowerCAmelCase__      :							Dict    =							{k: v for k, v in conversion_differences.items() if "hidden" not in k}
        lowerCAmelCase__      :							Optional[Any]    =							{k: v for k, v in conversion_differences.items() if "hidden" in k}
        if len(snake_case      ) == 0 and architectures is not None:
         raise ValueError(
             F"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
             " output was found. All outputs start with 'hidden'"      )
        lowerCAmelCase__      :							str    =							max(output_differences.values()      ) if output_differences else 0.0
        lowerCAmelCase__      :							List[str]    =							max(hidden_differences.values()      )
        if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
         raise ValueError(
             "The converted TensorFlow model has different outputs, something went wrong!\n"
             + F"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]      )
             + F"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
             + "\n".join([F"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]      )      )

        lowerCAmelCase__      :							str    =							"Update TF weights" if self._new_weights else "Add TF weights"
        if self._push:
         repo.git_add(auto_lfs_track=snake_case      )
         repo.git_commit(snake_case      )
         repo.git_push(blocking=snake_case      )  # this prints a progress bar with the upload
         self._logger.warning(F"""TF weights pushed into {self._model_name}"""      )
        elif not self._no_pr:
         self._logger.warning("Uploading the weights into a new PR..."      )
         lowerCAmelCase__      :							List[Any]    =							(
             "Model converted by the [`transformers`' `pt_to_tf`"
             " CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). "
             "All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"
             F"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
             F"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
             F"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
             F"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
         )
         if self._max_error > MAX_ERROR:
          commit_descrition += (
              F"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
          )
         if self._extra_commit_description:
          commit_descrition += "\n\n" + self._extra_commit_description

         # sharded model -> adds all related files (index and .h5 shards)
         if os.path.exists(snake_case      ):
          lowerCAmelCase__      :							Optional[int]    =							[
              CommitOperationAdd(path_in_repo=snake_case ,				path_or_fileobj=snake_case      )
          ]
          for shard_path in tf.io.gfile.glob(self._local_dir + "/tf_model-*.h5"      ):
           operations += [
               CommitOperationAdd(path_in_repo=os.path.basename(snake_case      ) ,				path_or_fileobj=snake_case      )
           ]
         else:
          lowerCAmelCase__      :							str    =							[CommitOperationAdd(path_in_repo=snake_case ,				path_or_fileobj=snake_case      )]

         lowerCAmelCase__      :							List[str]    =							create_commit(
             repo_id=self._model_name ,				operations=snake_case ,				commit_message=snake_case ,				commit_description=snake_case ,				repo_type="model" ,				create_pr=snake_case ,				).pr_url
         self._logger.warning(F"""PR open in {hub_pr_url}"""      )









"""simple docstring"""

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



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

 lowerCAmelCase__      :							Tuple    =							datasets.utils.logging.get_logger("transformers.modeling_utils"      )
 logger.addFilter(lowercase__      )
 try:
  yield
 finally:
  logger.removeFilter(lowercase__      )


_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  __a				(	datasets.Metric					):





       """simple docstring"""


       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case=None ,				snake_case=None ,				snake_case=None ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=64 ,				snake_case=4 ,				snake_case=False ,				snake_case=False ,				snake_case=None ,				snake_case=False ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Any    =							bert_score.utils.get_hash
        lowerCAmelCase__      :							List[Any]    =							bert_score.BERTScorer

        if version.parse(bert_score.__version__      ) >= version.parse("0.3.10"      ):
         lowerCAmelCase__      :							str    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
         lowerCAmelCase__      :							Tuple    =							functools.partial(snake_case ,				use_fast_tokenizer=snake_case      )
        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"
         lowerCAmelCase__      :							Dict    =							bert_score.utils.langamodel[lang.lower()]

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

        lowerCAmelCase__      :							Optional[int]    =							get_hash(
            model=snake_case ,				num_layers=snake_case ,				idf=snake_case ,				rescale_with_baseline=snake_case ,				use_custom_baseline=baseline_path is not None ,				)

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

        ((lowerCAmelCase__) ,	(lowerCAmelCase__) ,	(lowerCAmelCase__))      :							int    =							self.cached_bertscorer.score(
            cands=snake_case ,				refs=snake_case ,				verbose=snake_case ,				batch_size=snake_case ,				)
        lowerCAmelCase__      :							List[str]    =							{
            "precision": P.tolist(),
            "recall": R.tolist(),
            "f1": F.tolist(),
            "hashcode": hashcode,
        }
        return output_dict






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if references is not None:
         lowerCAmelCase__      :							Union[str, Any]    =							[[ref] if isinstance(snake_case ,				snake_case      ) else ref for ref in references]
        super().add_batch(predictions=snake_case ,				references=snake_case ,				**snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case=None ,				snake_case=None ,				**snake_case      ):





        """simple docstring"""

        if isinstance(snake_case ,				snake_case      ):
         lowerCAmelCase__      :							str    =							[reference]
        super().add(prediction=snake_case ,				reference=snake_case ,				**snake_case      )






