




"""simple docstring"""






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

from accelerate.utils.dataclasses import DistributedType






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase=2  ,      lowercase=3  ,      lowercase=64  ,      lowercase=None  ):
							_lowerCamelCase     :			Optional[int]    =    np.random.default_rng(lowercase  )
							_lowerCamelCase     :			Dict    =    length
							_lowerCamelCase     :			Dict    =    rng.normal(size=(length,)  ).astype(np.floataa  )
							_lowerCamelCase     :			Optional[int]    =    a * self.x + b + rng.normal(scale=0.1  ,      size=(length,)  ).astype(np.floataa  )
			def __len__(  self  ):
							return self.length






			def __getitem__(  self  ,      lowercase  ):
							return {"x": self.x[i], "y": self.y[i]}






class lowerCAmelCase__	(     torch.nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase=0  ,      lowercase=0  ,      lowercase=False  ):
							super().__init__()
							_lowerCamelCase     :			Union[str, Any]    =    torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
							_lowerCamelCase     :			Any    =    torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
							_lowerCamelCase     :			Dict    =    True






			def 					A_    (  self  ,      lowercase=None  ):


							if self.first_batch:
											print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
											_lowerCamelCase     :			int    =    False
							return x * self.a[0] + self.b[0]






class lowerCAmelCase__	(     torch.nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase=0  ,      lowercase=0  ,      lowercase=False  ):
							super().__init__()
							_lowerCamelCase     :			Dict    =    torch.nn.Parameter(torch.tensor(lowercase  ).float()  )
							_lowerCamelCase     :			List[Any]    =    torch.nn.Parameter(torch.tensor(lowercase  ).float()  )
							_lowerCamelCase     :			List[Any]    =    True






			def 					A_    (  self  ,      lowercase=None  ):


							if self.first_batch:
											print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''  )
											_lowerCamelCase     :			Union[str, Any]    =    False
							return x * self.a + self.b







def      _snake_case			(				lowercase__							,    lowercase__ = 16		):
				from datasets import load_dataset
				from transformers import AutoTokenizer

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

				_lowerCamelCase     :			List[str]    =    {v: i for i, v in enumerate(__lowercase		)}

				def tokenize_function(lowercase__		):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCamelCase     :			int    =    tokenizer(
								    examples['sentence1']							,    examples['sentence2']							,    truncation=__lowercase							,    max_length=__lowercase							,    padding='max_length'		)
								if "label" in examples:
												_lowerCamelCase     :			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
				_lowerCamelCase     :			List[Any]    =    datasets.map(
				    __lowercase							,    batched=__lowercase							,    remove_columns=['sentence1', 'sentence2', 'label']							,    )

				def collate_fn(lowercase__		):
								# 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(__lowercase							,    padding='max_length'							,    max_length=128							,    return_tensors='pt'		)
								return tokenizer.pad(__lowercase							,    padding='longest'							,    return_tensors='pt'		)

				# Instantiate dataloaders.
				_lowerCamelCase     :			str    =    DataLoader(tokenized_datasets['train']							,    shuffle=__lowercase							,    collate_fn=__lowercase							,    batch_size=2		)
				_lowerCamelCase     :			Any    =    DataLoader(tokenized_datasets['validation']							,    shuffle=__lowercase							,    collate_fn=__lowercase							,    batch_size=1		)

				return train_dataloader, eval_dataloader

import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
			import jax.numpy as jnp

			from transformers import FlaxXLMRobertaModel




@require_sentencepiece
@require_tokenizers
@require_flax
class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	@slow
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			AutoTokenizer.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			'The dog is cute and lives in the garden house'
				_snake_case		=			jnp.array([tokenizer.encode(lowercase	)]	)

				_snake_case		=			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
				_snake_case		=			jnp.array(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)

				_snake_case		=			model(lowercase	)['last_hidden_state']
				self.assertEqual(output.shape			,  lowercase	)
				# compare the actual values for a slice of last dim
				self.assertTrue(jnp.allclose(output[:, :, -1]			,  lowercase			,  atol=1E-3	)	)