




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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					"xglm"
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					["past_key_values"]

				SCREAMING_SNAKE_CASE__					:							str	      =					{
				    "num_attention_heads": "attention_heads",
				    "hidden_size": "d_model",
				    "num_hidden_layers": "num_layers",
				}


				def __init__(							self:   int		,					_lowerCamelCase:   List[str]=25_60_08		,					_lowerCamelCase:   Any=20_48		,					_lowerCamelCase:   List[str]=10_24		,					_lowerCamelCase:   List[str]=40_96		,					_lowerCamelCase:   List[str]=24		,					_lowerCamelCase:   List[Any]=16		,					_lowerCamelCase:   int="gelu"		,					_lowerCamelCase:   List[str]=0.1		,					_lowerCamelCase:   List[Any]=0.1		,					_lowerCamelCase:   Tuple=0.0		,					_lowerCamelCase:   List[Any]=0.0		,					_lowerCamelCase:   Union[str, Any]=0.02		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   int=True		,					_lowerCamelCase:   List[str]=2		,					_lowerCamelCase:   List[str]=1		,					_lowerCamelCase:   Optional[int]=0		,					_lowerCamelCase:   int=2		,					**_lowerCamelCase:   Tuple		,					):
											SCREAMING_SNAKE_CASE_   	=    vocab_size
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    d_model
											SCREAMING_SNAKE_CASE_   	=    ffn_dim
											SCREAMING_SNAKE_CASE_   	=    num_layers
											SCREAMING_SNAKE_CASE_   	=    attention_heads
											SCREAMING_SNAKE_CASE_   	=    activation_function
											SCREAMING_SNAKE_CASE_   	=    dropout
											SCREAMING_SNAKE_CASE_   	=    attention_dropout
											SCREAMING_SNAKE_CASE_   	=    activation_dropout
											SCREAMING_SNAKE_CASE_   	=    layerdrop
											SCREAMING_SNAKE_CASE_   	=    init_std
											SCREAMING_SNAKE_CASE_   	=    scale_embedding  # scale factor will be sqrt(d_model) if True
											SCREAMING_SNAKE_CASE_   	=    use_cache

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








import re
import string

import numpy as np

import datasets


__SCREAMING_SNAKE_CASE 						="""
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""

__SCREAMING_SNAKE_CASE 						="""
Args:
    predictions: List of predicted texts.
    references: List of reference texts.
    regexes_to_ignore: List, defaults to None. Regex expressions of characters to
        ignore when calculating the exact matches. Note: these regexes are removed
        from the input data before the changes based on the options below (e.g. ignore_case,
        ignore_punctuation, ignore_numbers) are applied.
    ignore_case: Boolean, defaults to False. If true, turns everything
        to lowercase so that capitalization differences are ignored.
    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
        comparing predictions and references.
Returns:
    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    25.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    50.0


    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
    >>> print(round(results[\"exact_match\"], 1))
    75.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
    >>> print(round(results[\"exact_match\"], 1))
    100.0

    >>> exact_match = datasets.load_metric(\"exact_match\")
    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
    >>> results = exact_match.compute(references=refs, predictions=preds)
    >>> print(round(results[\"exact_match\"], 1))
    33.3

"""

__SCREAMING_SNAKE_CASE 						="""
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,  _KWARGS_DESCRIPTION)
class  __magic_name__						(  datasets.Metric):
				'''simple docstring'''







				def     _A							(							self:   Optional[int]		):
											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'''		),
											        }		)		,					reference_urls=[]		,					)





				def     _A							(							self:   Tuple		,					_lowerCamelCase:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Tuple=None		,					_lowerCamelCase:   str=False		,					_lowerCamelCase:   List[str]=False		,					_lowerCamelCase:   List[str]=False		,					):
											if regexes_to_ignore is not None:
																		for s in regexes_to_ignore:
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in predictions]		)
																									SCREAMING_SNAKE_CASE_   	=    np.array([re.sub(_lowerCamelCase		,					''''''		,					_lowerCamelCase		) for x in references]		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)

											if ignore_case:
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.lower(_lowerCamelCase		)

											if ignore_punctuation:
																		SCREAMING_SNAKE_CASE_   	=    string.punctuation.maketrans(''''''		,					''''''		,					string.punctuation		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											if ignore_numbers:
																		SCREAMING_SNAKE_CASE_   	=    string.digits.maketrans(''''''		,					''''''		,					string.digits		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    np.char.translate(_lowerCamelCase		,					table=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    predictions == references

											return {"exact_match": np.mean(_lowerCamelCase		) * 1_00}


