


'''simple docstring'''







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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType


lowerCAmelCase     :  Dict											=    logging.get_logger(__name__)

lowerCAmelCase     :  List[str]											=    {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "layoutlmv3"







     def __init__(			self   ,						snake_case__=5_0265   ,						snake_case__=768   ,						snake_case__=12   ,						snake_case__=12   ,						snake_case__=3072   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=2   ,						snake_case__=0.02   ,						snake_case__=1E-5   ,						snake_case__=1   ,						snake_case__=0   ,						snake_case__=2   ,						snake_case__=1024   ,						snake_case__=128   ,						snake_case__=128   ,						snake_case__=True   ,						snake_case__=32   ,						snake_case__=128   ,						snake_case__=64   ,						snake_case__=256   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=224   ,						snake_case__=3   ,						snake_case__=16   ,						snake_case__=None   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                vocab_size=snake_case__   ,						hidden_size=snake_case__   ,						num_hidden_layers=snake_case__   ,						num_attention_heads=snake_case__   ,						intermediate_size=snake_case__   ,						hidden_act=snake_case__   ,						hidden_dropout_prob=snake_case__   ,						attention_probs_dropout_prob=snake_case__   ,						max_position_embeddings=snake_case__   ,						type_vocab_size=snake_case__   ,						initializer_range=snake_case__   ,						layer_norm_eps=snake_case__   ,						pad_token_id=snake_case__   ,						bos_token_id=snake_case__   ,						eos_token_id=snake_case__   ,						**snake_case__   ,						)
            _lowerCAmelCase     :				List[Any]       =							max_ad_position_embeddings
            _lowerCAmelCase     :				List[str]       =							coordinate_size
            _lowerCAmelCase     :				int       =							shape_size
            _lowerCAmelCase     :				int       =							has_relative_attention_bias
            _lowerCAmelCase     :				Optional[Any]       =							rel_pos_bins
            _lowerCAmelCase     :				int       =							max_rel_pos
            _lowerCAmelCase     :				Optional[int]       =							has_spatial_attention_bias
            _lowerCAmelCase     :				Tuple       =							rel_ad_pos_bins
            _lowerCAmelCase     :				Optional[Any]       =							max_rel_ad_pos
            _lowerCAmelCase     :				str       =							text_embed
            _lowerCAmelCase     :				Optional[int]       =							visual_embed
            _lowerCAmelCase     :				int       =							input_size
            _lowerCAmelCase     :				Dict       =							num_channels
            _lowerCAmelCase     :				Union[str, Any]       =							patch_size
            _lowerCAmelCase     :				Optional[int]       =							classifier_dropout
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     version.parse("1.12"						)







     @property
     def a						(			self ):

            '''simple docstring'''

            if self.task in ["question-answering", "sequence-classification"]:
                   return OrderedDict(
                       [
                           ('input_ids', {0: 'batch', 1: 'sequence'}),
                           ('attention_mask', {0: 'batch', 1: 'sequence'}),
                           ('bbox', {0: 'batch', 1: 'sequence'}),
                           ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                       ] )
            else:
                   return OrderedDict(
                       [
                           ('input_ids', {0: 'batch', 1: 'sequence'}),
                           ('bbox', {0: 'batch', 1: 'sequence'}),
                           ('attention_mask', {0: 'batch', 1: 'sequence'}),
                           ('pixel_values', {0: 'batch', 1: 'num_channels'}),
                       ] )







     @property
     def a						(			self ):

            '''simple docstring'''

            return 1E-5







     @property
     def a						(			self ):

            '''simple docstring'''

            return 12







     def a						(			self   ,						snake_case__   ,						snake_case__ = -1   ,						snake_case__ = -1   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = 3   ,						snake_case__ = 40   ,						snake_case__ = 40   ,						):

            '''simple docstring'''

            setattr(processor.image_processor   ,						'apply_ocr'   ,						snake_case__ )

            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            _lowerCAmelCase     :				List[str]       =							compute_effective_axis_dimension(
                snake_case__   ,						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
            _lowerCAmelCase     :				int       =							processor.tokenizer.num_special_tokens_to_add(snake_case__ )
            _lowerCAmelCase     :				int       =							compute_effective_axis_dimension(
                snake_case__   ,						fixed_dimension=OnnxConfig.default_fixed_sequence   ,						num_token_to_add=snake_case__ )
            # Generate dummy inputs according to compute batch and sequence
            _lowerCAmelCase     :				List[Any]       =							[[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size

            # Generate dummy bounding boxes
            _lowerCAmelCase     :				Dict       =							[[[48, 84, 73, 128]]] * batch_size

            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            _lowerCAmelCase     :				Optional[Any]       =							self._generate_dummy_images(snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ )

            _lowerCAmelCase     :				Optional[Any]       =							dict(
                processor(
                    snake_case__   ,						text=snake_case__   ,						boxes=snake_case__   ,						return_tensors=snake_case__   ,						) )

            return inputs





'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

