


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A	=logging.get_logger(__name__)

__A	={
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}


class 		_snake_case					(     __SCREAMING_SNAKE_CASE	):
					lowerCAmelCase      :List[str]   =					'''camembert'''




					def __init__(      self							,							_lowerCamelCase=3_0522							,							_lowerCamelCase=768							,							_lowerCamelCase=12							,							_lowerCamelCase=12							,							_lowerCamelCase=3072							,							_lowerCamelCase="gelu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=512							,							_lowerCamelCase=2							,							_lowerCamelCase=0.02							,							_lowerCamelCase=1e-1_2							,							_lowerCamelCase=1							,							_lowerCamelCase=0							,							_lowerCamelCase=2							,							_lowerCamelCase="absolute"							,							_lowerCamelCase=True							,							_lowerCamelCase=None							,							**_lowerCamelCase							,							):
								super().__init__(pad_token_id=__snake_case							,							bos_token_id=__snake_case							,							eos_token_id=__snake_case							,							**__snake_case)

								UpperCAmelCase__  :					Optional[Any]        =     vocab_size
								UpperCAmelCase__  :					List[str]        =     hidden_size
								UpperCAmelCase__  :					str        =     num_hidden_layers
								UpperCAmelCase__  :					str        =     num_attention_heads
								UpperCAmelCase__  :					Optional[Any]        =     hidden_act
								UpperCAmelCase__  :					Optional[Any]        =     intermediate_size
								UpperCAmelCase__  :					Tuple        =     hidden_dropout_prob
								UpperCAmelCase__  :					Dict        =     attention_probs_dropout_prob
								UpperCAmelCase__  :					Union[str, Any]        =     max_position_embeddings
								UpperCAmelCase__  :					Optional[int]        =     type_vocab_size
								UpperCAmelCase__  :					Any        =     initializer_range
								UpperCAmelCase__  :					List[Any]        =     layer_norm_eps
								UpperCAmelCase__  :					Tuple        =     position_embedding_type
								UpperCAmelCase__  :					Tuple        =     use_cache
								UpperCAmelCase__  :					Dict        =     classifier_dropout


class 		_snake_case					(     __SCREAMING_SNAKE_CASE	):




					@property
					def 				snake_case__						(      self):
								if self.task == "multiple-choice":
											UpperCAmelCase__  :					List[str]        =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
											UpperCAmelCase__  :					Dict        =     {0: '''batch''', 1: '''sequence'''}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ])



'''simple docstring'''



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 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     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]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))