



"""simple docstring"""







import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class        __A       :







		'''simple docstring'''





		def __init__(	self						:  Dict			,_snake_case						:  Union[str, Any]=2			,_snake_case						:  str=3			,_snake_case						:  Optional[Any]=64			,_snake_case						:  Optional[int]=None						)					->		int:






				"""simple docstring"""



				lowercase__   :    Any				=			np.random.default_rng(_snake_case						)
				lowercase__   :    str				=			length
				lowercase__   :    List[str]				=			rng.normal(size=(length,)						).astype(np.floataa						)
				lowercase__   :    Dict				=			a * self.x + b + rng.normal(scale=0.1			,size=(length,)						).astype(np.floataa						)







		def __len__(	self						:  str						)					->		int:






				"""simple docstring"""



				return self.length







		def __getitem__(	self						:  Tuple			,_snake_case						:  Tuple						)					->		List[Any]:






				"""simple docstring"""



				return {"x": self.x[i], "y": self.y[i]}





class        __A       (	torch.nn.Module    ):







		'''simple docstring'''





		def __init__(	self						:  Union[str, Any]			,_snake_case						:  Tuple=0			,_snake_case						:  Optional[Any]=0			,_snake_case						:  Optional[int]=False						)					->		Optional[Any]:






				"""simple docstring"""



				super().__init__()
				lowercase__   :    int				=			torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
				lowercase__   :    int				=			torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
				lowercase__   :    Tuple				=			True







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple=None						)					->		str:






				"""simple docstring"""



				if self.first_batch:
						print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""						)
						lowercase__   :    Any				=			False
				return x * self.a[0] + self.b[0]





class        __A       (	torch.nn.Module    ):







		'''simple docstring'''





		def __init__(	self						:  Optional[int]			,_snake_case						:  List[Any]=0			,_snake_case						:  Optional[Any]=0			,_snake_case						:  Optional[Any]=False						)					->		Any:






				"""simple docstring"""



				super().__init__()
				lowercase__   :    int				=			torch.nn.Parameter(torch.tensor(_snake_case						).float()						)
				lowercase__   :    Tuple				=			torch.nn.Parameter(torch.tensor(_snake_case						).float()						)
				lowercase__   :    Any				=			True







		def 					UpperCAmelCase    (	self						:  int			,_snake_case						:  Optional[Any]=None						)					->		Tuple:






				"""simple docstring"""



				if self.first_batch:
						print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""						)
						lowercase__   :    List[str]				=			False
				return x * self.a + self.b





def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = 16	)							->      Optional[Any]:
		from datasets import load_dataset
		from transformers import AutoTokenizer

		lowercase__   :    List[str]				=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
		lowercase__   :    Tuple				=			{'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
		lowercase__   :    str				=			load_dataset('''csv'''						,     data_files=__lowerCamelCase	)
		lowercase__   :    Union[str, Any]				=			datasets['''train'''].unique('''label'''	)

		lowercase__   :    List[Any]				=			{v: i for i, v in enumerate(__lowerCamelCase	)}

		def tokenize_function(__lowerCamelCase	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__   :    int				=			tokenizer(
				    examples['''sentence1''']						,     examples['''sentence2''']						,     truncation=__lowerCamelCase						,     max_length=__lowerCamelCase						,     padding='''max_length'''	)
				if "label" in examples:
						lowercase__   :    str				=			[label_to_id[l] for l in examples['''label''']]
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowercase__   :    Any				=			datasets.map(
		    __lowerCamelCase						,     batched=__lowerCamelCase						,     remove_columns=['''sentence1''', '''sentence2''', '''label''']						,     )

		def collate_fn(__lowerCamelCase	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(__lowerCamelCase						,     padding='''max_length'''						,     max_length=1_28						,     return_tensors='''pt'''	)
				return tokenizer.pad(__lowerCamelCase						,     padding='''longest'''						,     return_tensors='''pt'''	)

		# Instantiate dataloaders.
		lowercase__   :    int				=			DataLoader(tokenized_datasets['''train''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=2	)
		lowercase__   :    Optional[Any]				=			DataLoader(tokenized_datasets['''validation''']						,     shuffle=__lowerCamelCase						,     collate_fn=__lowerCamelCase						,     batch_size=1	)

		return train_dataloader, eval_dataloader





"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase_   						=   '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase_   						=   '\nComputes METEOR score of translated segments against one or more 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    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION    )
class        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""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/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']			,reference_urls=[
				        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
				        '''https://en.wikipedia.org/wiki/METEOR''',
				    ]			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				import nltk

				nltk.download('''wordnet'''						)
				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						nltk.download('''punkt'''						)
				if NLTK_VERSION >= version.Version('''3.6.6'''						):
						nltk.download('''omw-1.4'''						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

				return {"meteor": np.mean(_snake_case						)}
