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


snake_case :		int							      =  logging.get_logger(__name__)

snake_case :		Union[str, Any]							      =  {
    "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json",
    "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/config.json",
    "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/config.json",
    "funnel-transformer/medium-base": "https://huggingface.co/funnel-transformer/medium-base/resolve/main/config.json",
    "funnel-transformer/intermediate": (
        "https://huggingface.co/funnel-transformer/intermediate/resolve/main/config.json"
    ),
    "funnel-transformer/intermediate-base": (
        "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/config.json"
    ),
    "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/config.json",
    "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/config.json",
    "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/config.json",
    "funnel-transformer/xlarge-base": "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/config.json",
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'funnel'
			UpperCamelCase__          =    {
			    'hidden_size': 'd_model',
			    'num_attention_heads': 'n_head',
			}







			def __init__(				self						,   _a=30_522						,   _a=[4, 4, 4]						,   _a=None						,   _a=2						,   _a=768						,   _a=12						,   _a=64						,   _a=3_072						,   _a="gelu_new"						,   _a=0.1						,   _a=0.1						,   _a=0.0						,   _a=0.1						,   _a=None						,   _a=1e-9						,   _a="mean"						,   _a="relative_shift"						,   _a=True						,   _a=True						,   _a=True						,   **_a						,   ):
				__magic_name__		:		List[Any]	   =     vocab_size
				__magic_name__		:		Tuple	   =     block_sizes
				__magic_name__		:		Tuple	   =     [1] * len(_a		) if block_repeats is None else block_repeats
				assert len(_a		) == len(
				    self.block_repeats		), "`block_sizes` and `block_repeats` should have the same length."
				__magic_name__		:		str	   =     num_decoder_layers
				__magic_name__		:		str	   =     d_model
				__magic_name__		:		Dict	   =     n_head
				__magic_name__		:		Tuple	   =     d_head
				__magic_name__		:		str	   =     d_inner
				__magic_name__		:		Optional[Any]	   =     hidden_act
				__magic_name__		:		Any	   =     hidden_dropout
				__magic_name__		:		Dict	   =     attention_dropout
				__magic_name__		:		Any	   =     activation_dropout
				__magic_name__		:		Tuple	   =     initializer_range
				__magic_name__		:		Optional[int]	   =     initializer_std
				__magic_name__		:		List[str]	   =     layer_norm_eps
				assert pooling_type in [
				    "mean",
				    "max",
				], f'''Got {pooling_type} for `pooling_type` but only \'mean\' and \'max\' are supported.'''
				__magic_name__		:		Any	   =     pooling_type
				assert attention_type in [
				    "relative_shift",
				    "factorized",
				], f'''Got {attention_type} for `attention_type` but only \'relative_shift\' and \'factorized\' are supported.'''
				__magic_name__		:		Any	   =     attention_type
				__magic_name__		:		int	   =     separate_cls
				__magic_name__		:		Dict	   =     truncate_seq
				__magic_name__		:		List[Any]	   =     pool_q_only

				super().__init__(**_a		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return sum(self.block_sizes		)







			@num_hidden_layers.setter
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				raise NotImplementedError(
				    "This model does not support the setting of `num_hidden_layers`. Please set `block_sizes`."		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.block_sizes		)







			@num_blocks.setter
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				raise NotImplementedError("This model does not support the setting of `num_blocks`. Please set `block_sizes`."		)


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


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

snake_case :		Any							      =  "\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n"

snake_case :		str							      =  "\nCalculates average rouge scores for a list of hypotheses and references\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,\n        `\"rougeL\"`: Longest common subsequence based scoring.\n        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric('rouge')\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']\n    >>> print(results[\"rouge1\"])\n    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))\n    >>> print(results[\"rouge1\"].mid.fmeasure)\n    1.0\n"





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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				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 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a=None						,   _a=True						,   _a=False		):
				if rouge_types is None:
					__magic_name__		:		str	   =     ["rouge1", "rouge2", "rougeL", "rougeLsum"]

				__magic_name__		:		List[str]	   =     rouge_scorer.RougeScorer(rouge_types=_a						,   use_stemmer=_a		)
				if use_aggregator:
					__magic_name__		:		Dict	   =     scoring.BootstrapAggregator()
				else:
					__magic_name__		:		str	   =     []

				for ref, pred in zip(_a						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     scorer.score(_a						,   _a		)
					if use_aggregator:
						aggregator.add_scores(_a		)
					else:
						scores.append(_a		)

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

				return result
