


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__							:						Union[str, Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						Optional[int]							     =   {
    """microsoft/trocr-base-handwritten""": (
        """https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Union[str, Any]	   =       'trocr'
     __lowercase				:							Optional[Any]	   =       ['past_key_values']
     __lowercase				:							Union[str, Any]	   =       {
         'num_attention_heads': 'decoder_attention_heads',
         'hidden_size': 'd_model',
         'num_hidden_layers': 'decoder_layers',
     }

     def __init__(					self:Optional[Any]     ,		_a:Optional[int]=5_02_65     ,		_a:List[Any]=10_24     ,		_a:Union[str, Any]=12     ,		_a:Any=16     ,		_a:int=40_96     ,		_a:int="gelu"     ,		_a:Union[str, Any]=5_12     ,		_a:str=0.1     ,		_a:Dict=0.0     ,		_a:int=0.0     ,		_a:int=2     ,		_a:Union[str, Any]=0.02     ,		_a:List[Any]=0.0     ,		_a:Any=True     ,		_a:Optional[Any]=False     ,		_a:Union[str, Any]=True     ,		_a:List[Any]=True     ,		_a:Any=1     ,		_a:str=0     ,		_a:Optional[int]=2     ,		**_a:int     ,		):
       snake_case__						= vocab_size
       snake_case__						= d_model
       snake_case__						= decoder_layers
       snake_case__						= decoder_attention_heads
       snake_case__						= decoder_ffn_dim
       snake_case__						= activation_function
       snake_case__						= max_position_embeddings
       snake_case__						= dropout
       snake_case__						= attention_dropout
       snake_case__						= activation_dropout
       snake_case__						= init_std
       snake_case__						= decoder_layerdrop
       snake_case__						= use_cache
       snake_case__						= scale_embedding
       snake_case__						= use_learned_position_embeddings
       snake_case__						= layernorm_embedding

       super().__init__(
           pad_token_id=_a     ,		bos_token_id=_a     ,		eos_token_id=_a     ,		decoder_start_token_id=_a     ,		**_a     ,		)




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_lowerCamelCase      :					List[str]					    =      version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
   from nltk import word_tokenize


_lowerCamelCase      :					Dict					    =      '''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_lowerCamelCase      :					str					    =      '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_lowerCamelCase      :					Union[str, Any]					    =      '''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''





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



       '''simple docstring'''







       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return datasets.MetricInfo(
                 description=_DESCRIPTION ,   citation=_CITATION ,   inputs_description=_KWARGS_DESCRIPTION ,   features=datasets.Features(
                     {
                         '''predictions''': datasets.Value('''string''' ,   id='''sequence'''     ),
                         '''references''': datasets.Value('''string''' ,   id='''sequence'''     ),
                     }     ) ,   codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py'''] ,   reference_urls=[
                     '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
                     '''https://en.wikipedia.org/wiki/METEOR''',
                 ] ,   )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             import nltk

             nltk.download('''wordnet'''     )
             if NLTK_VERSION >= version.Version('''3.6.5'''     ):
                   nltk.download('''punkt'''     )
             if NLTK_VERSION >= version.Version('''3.6.6'''     ):
                   nltk.download('''omw-1.4'''     )


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__=0.9 ,   lowercase__=3 ,   lowercase__=0.5     ):







             '''simple docstring'''



             if NLTK_VERSION >= version.Version('''3.6.5'''     ):
                   __A    =[
                       meteor_score.single_meteor_score(
                           word_tokenize(lowercase__     ) ,   word_tokenize(lowercase__     ) ,   alpha=lowercase__ ,   beta=lowercase__ ,   gamma=lowercase__     )
                       for ref, pred in zip(lowercase__ ,   lowercase__     )
                   ]
             else:
                   __A    =[
                       meteor_score.single_meteor_score(lowercase__ ,   lowercase__ ,   alpha=lowercase__ ,   beta=lowercase__ ,   gamma=lowercase__     )
                       for ref, pred in zip(lowercase__ ,   lowercase__     )
                   ]

             return {"meteor": np.mean(lowercase__     )}


