



"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase       :					Union[str, Any]      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Union[str, Any]      					=					{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''ibert'''





		def __init__(					self							,    _lowercase=3_0_5_2_2							,    _lowercase=7_6_8							,    _lowercase=1_2							,    _lowercase=1_2							,    _lowercase=3_0_7_2							,    _lowercase="gelu"							,    _lowercase=0.1							,    _lowercase=0.1							,    _lowercase=5_1_2							,    _lowercase=2							,    _lowercase=0.02							,    _lowercase=1E-12							,    _lowercase=1							,    _lowercase=0							,    _lowercase=2							,    _lowercase="absolute"							,    _lowercase=False							,    _lowercase="none"							,    **_lowercase							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								super().__init__(pad_token_id=_lowercase							,    bos_token_id=_lowercase							,    eos_token_id=_lowercase							,    **_lowercase   )

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




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@property
		def    UpperCAmelCase__							(					self   ) ->						Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								if self.task == "multiple-choice":
														snake_case_   : List[Any]						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
														snake_case_   : List[str]						=			{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ]   )






"""simple docstring"""





import unittest

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


if is_flax_available():
					import optax
					from flax.training.common_utils import onehot

					from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
					from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		@slow
		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : int						=			FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""   )
								snake_case_   : List[Any]						=			AutoTokenizer.from_pretrained("""google/mt5-small"""   )

								snake_case_   : Dict						=			tokenizer("""Hello there"""							,    return_tensors="""np"""   ).input_ids
								snake_case_   : List[str]						=			tokenizer("""Hi I am"""							,    return_tensors="""np"""   ).input_ids

								snake_case_   : Optional[Any]						=			shift_tokens_right(_lowercase							,    model.config.pad_token_id							,    model.config.decoder_start_token_id   )

								snake_case_   : Tuple						=			model(_lowercase							,    decoder_input_ids=_lowercase   ).logits
								snake_case_   : Tuple						=			optax.softmax_cross_entropy(_lowercase							,    onehot(_lowercase							,    logits.shape[-1]   )   ).mean()

								snake_case_   : List[str]						=			-(labels.shape[-1] * loss.item())

								snake_case_   : Optional[int]						=			-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )

