





"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     int     =		[
	    'decoder.version',
	    'decoder.output_projection.weight',
	    '_float_tensor',
	    'decoder.embed_positions._float_tensor',
	]
	for k in ignore_keys:
		state_dict.pop(__a				,	__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		emb.weight.shape
	snake_case_						:     str     =		nn.Linear(__a				,	__a				,	bias=__a				)
	snake_case_						:     List[str]     =		emb.weight.data
	return lin_layer


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Dict     =		torch.load(__a				,	map_location='cpu'				)
	snake_case_						:     Optional[Any]     =		Namespace(**checkpoint['cfg']['model']				)
	snake_case_						:     List[Any]     =		checkpoint['model']
	remove_ignore_keys_(__a				)
	snake_case_						:     Dict     =		state_dict['decoder.embed_tokens.weight'].shape[0]

	snake_case_						:     Union[str, Any]     =		{key.replace('decoder'				,	'model'				): val for key, val in state_dict.items()}

	snake_case_						:     List[str]     =		XGLMConfig(
	    vocab_size=__a				,	max_position_embeddings=args.max_target_positions				,	num_layers=args.decoder_layers				,	attention_heads=args.decoder_attention_heads				,	ffn_dim=args.decoder_ffn_embed_dim				,	d_model=args.decoder_embed_dim				,	layerdrop=args.decoder_layerdrop				,	dropout=args.dropout				,	attention_dropout=args.attention_dropout				,	activation_dropout=args.activation_dropout				,	activation_function='gelu'				,	scale_embedding=not args.no_scale_embedding				,	tie_word_embeddings=args.share_decoder_input_output_embed				,	)

	snake_case_						:     Tuple     =		XGLMForCausalLM(__a				)
	snake_case_						:     Optional[Any]     =		model.load_state_dict(__a				,	strict=__a				)
	print(__a				)
	snake_case_						:     int     =		make_linear_from_emb(model.model.embed_tokens				)

	return model


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
						parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()
						_SCREAMING_SNAKE_CASE 		=						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)








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 SCREAMING_SNAKE_CASE_  (   datasets.Metric   ):






						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Optional[Any]:







							"""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'		),
							        }		)						,				reference_urls=[]						,				)






						def UpperCAmelCase_			( self : List[Any]						,				_A : Optional[Any]						,				_A : Optional[int]						,				_A : Optional[int]=None						,				_A : Dict=False						,				_A : Dict=False						,				_A : Optional[Any]=False						,				)							->      List[str]:







							"""simple docstring"""


							if regexes_to_ignore is not None:
								for s in regexes_to_ignore:
									snake_case_						:     List[str]     =		np.array([re.sub(_A						,				''						,				_A		) for x in predictions]		)
									snake_case_						:     int     =		np.array([re.sub(_A						,				''						,				_A		) for x in references]		)
							else:
								snake_case_						:     Optional[Any]     =		np.asarray(_A		)
								snake_case_						:     Optional[Any]     =		np.asarray(_A		)

							if ignore_case:
								snake_case_						:     int     =		np.char.lower(_A		)
								snake_case_						:     List[str]     =		np.char.lower(_A		)

							if ignore_punctuation:
								snake_case_						:     str     =		string.punctuation.maketrans(''						,				''						,				string.punctuation		)
								snake_case_						:     str     =		np.char.translate(_A						,				table=_A		)
								snake_case_						:     Any     =		np.char.translate(_A						,				table=_A		)

							if ignore_numbers:
								snake_case_						:     int     =		string.digits.maketrans(''						,				''						,				string.digits		)
								snake_case_						:     Tuple     =		np.char.translate(_A						,				table=_A		)
								snake_case_						:     Optional[Any]     =		np.char.translate(_A						,				table=_A		)

							snake_case_						:     Optional[Any]     =		predictions == references

							return {"exact_match": np.mean(_A		) * 100}

