"""simple docstring"""






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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


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

__lowerCAmelCase					: str        ={
    """deepmind/language-perceiver""": """https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json""",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Dict													=			'perceiver'

      def __init__(		self	, __lowerCAmelCase=256	, __lowerCAmelCase=1280	, __lowerCAmelCase=768	, __lowerCAmelCase=1	, __lowerCAmelCase=26	, __lowerCAmelCase=8	, __lowerCAmelCase=8	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase="kv"	, __lowerCAmelCase=1	, __lowerCAmelCase=1	, __lowerCAmelCase="gelu"	, __lowerCAmelCase=0.1	, __lowerCAmelCase=0.0_2	, __lowerCAmelCase=1E-12	, __lowerCAmelCase=True	, __lowerCAmelCase=262	, __lowerCAmelCase=2048	, __lowerCAmelCase=56	, __lowerCAmelCase=[368, 496]	, __lowerCAmelCase=16	, __lowerCAmelCase=1920	, __lowerCAmelCase=16	, __lowerCAmelCase=[1, 16, 224, 224]	, **__lowerCAmelCase	, ):







            """simple docstring"""



            super().__init__(**__lowerCAmelCase		)

            lowercase														=			num_latents
            lowercase														=			d_latents
            lowercase														=			d_model
            lowercase														=			num_blocks
            lowercase														=			num_self_attends_per_block
            lowercase														=			num_self_attention_heads
            lowercase														=			num_cross_attention_heads
            lowercase														=			qk_channels
            lowercase														=			v_channels
            lowercase														=			cross_attention_shape_for_attention
            lowercase														=			self_attention_widening_factor
            lowercase														=			cross_attention_widening_factor
            lowercase														=			hidden_act
            lowercase														=			attention_probs_dropout_prob
            lowercase														=			initializer_range
            lowercase														=			layer_norm_eps
            lowercase														=			use_query_residual
            # masked language modeling attributes
            lowercase														=			vocab_size
            lowercase														=			max_position_embeddings
            # image classification attributes
            lowercase														=			image_size
            # flow attributes
            lowercase														=			train_size
            # multimodal autoencoding attributes
            lowercase														=			num_frames
            lowercase														=			audio_samples_per_frame
            lowercase														=			samples_per_patch
            lowercase														=			output_shape






class 				_A		( lowerCAmelCase			):

      @property
      def        A__ (		self		):







            """simple docstring"""



            if self.task == "multiple-choice":
                  lowercase														=			{0: """batch""", 1: """choice""", 2: """sequence"""}
            else:
                  lowercase														=			{0: """batch""", 1: """sequence"""}
            return OrderedDict(
                [
                    ("""inputs""", dynamic_axis),
                    ("""attention_mask""", dynamic_axis),
                ]		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            return 1E-4







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = -1	, __lowerCAmelCase = False	, __lowerCAmelCase = None	, __lowerCAmelCase = 3	, __lowerCAmelCase = 40	, __lowerCAmelCase = 40	, ):







            """simple docstring"""



            if isinstance(__lowerCAmelCase	, __lowerCAmelCase		):
                  # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                  lowercase														=			compute_effective_axis_dimension(
                      __lowerCAmelCase	, 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
                  lowercase														=			preprocessor.num_special_tokens_to_add(__lowerCAmelCase		)
                  lowercase														=			compute_effective_axis_dimension(
                      __lowerCAmelCase	, fixed_dimension=OnnxConfig.default_fixed_sequence	, num_token_to_add=__lowerCAmelCase		)
                  # Generate dummy inputs according to compute batch and sequence
                  lowercase														=			[""" """.join(["""a"""]		) * seq_length] * batch_size
                  lowercase														=			dict(preprocessor(__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)		)
                  lowercase														=			inputs.pop("""input_ids"""		)
                  return inputs
            elif isinstance(__lowerCAmelCase	, __lowerCAmelCase		) and preprocessor.model_input_names[0] == "pixel_values":
                  # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
                  lowercase														=			compute_effective_axis_dimension(__lowerCAmelCase	, fixed_dimension=OnnxConfig.default_fixed_batch		)
                  lowercase														=			self._generate_dummy_images(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			dict(preprocessor(images=__lowerCAmelCase	, return_tensors=__lowerCAmelCase		)		)
                  lowercase														=			inputs.pop("""pixel_values"""		)
                  return inputs
            else:
                  raise ValueError(
                      """Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."""		)




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and 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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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

      def        A__ (		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/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



