


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
			import numpy as np
			import tensorflow as tf

			from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@slow
							def        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case						:     List[str]									=  TFCamembertModel.from_pretrained("jplu/tf-camembert-base"							)

														__snake_case						:     str									=  tf.convert_to_tensor(
														    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,      dtype=tf.intaa       ,      )  # J'aime le camembert !"

														__snake_case						:     str									=  model(lowerCamelCase							)["last_hidden_state"]
														__snake_case						:     Union[str, Any]									=  tf.TensorShape((1, 10, 768)							)
														self.assertEqual(output.shape       ,      lowerCamelCase							)
														# compare the actual values for a slice.
														__snake_case						:     Tuple									=  tf.convert_to_tensor(
														    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      dtype=tf.floataa       ,      )
														# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
														# camembert.eval()
														# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

														self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,      expected_slice.numpy()       ,      atol=1E-4							)							)





'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							field(
		    default=0.0      ,						metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Whether to SortishSamler or not.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'whether to use adafactor'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}   )
		_a       					=							field(default=A__      ,						metadata={'help': 'Dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default=A__      ,						metadata={'help': 'Attention dropout probability. Goes into model.config.'}   )
		_a       					=							field(
		    default='linear'      ,						metadata={'help': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}'''}      ,						)