
"""simple docstring"""





from ....configuration_utils import PretrainedConfig
from ....utils import logging


__magic_name__			=     logging.get_logger(__name__)

# TODO: upload to AWS
__magic_name__			=     {
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}
class 		SCREAMING_SNAKE_CASE_					(		SCREAMING_SNAKE_CASE__					):




		"""simple docstring"""





		__lowercase				:    Tuple		  =						'''retribert'''




		def __init__(	self				,    lowerCAmelCase__=3_0_5_2_2				,    lowerCAmelCase__=7_6_8				,    lowerCAmelCase__=8				,    lowerCAmelCase__=1_2				,    lowerCAmelCase__=3_0_7_2				,    lowerCAmelCase__="gelu"				,    lowerCAmelCase__=0.1				,    lowerCAmelCase__=0.1				,    lowerCAmelCase__=5_1_2				,    lowerCAmelCase__=2				,    lowerCAmelCase__=0.02				,    lowerCAmelCase__=1E-12				,    lowerCAmelCase__=True				,    lowerCAmelCase__=1_2_8				,    lowerCAmelCase__=0				,    **lowerCAmelCase__				,    ):
				super().__init__(pad_token_id=lowerCAmelCase__				,    **lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE				=					vocab_size
				__SCREAMING_SNAKE_CASE				=					hidden_size
				__SCREAMING_SNAKE_CASE				=					num_hidden_layers
				__SCREAMING_SNAKE_CASE				=					num_attention_heads
				__SCREAMING_SNAKE_CASE				=					hidden_act
				__SCREAMING_SNAKE_CASE				=					intermediate_size
				__SCREAMING_SNAKE_CASE				=					hidden_dropout_prob
				__SCREAMING_SNAKE_CASE				=					attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE				=					max_position_embeddings
				__SCREAMING_SNAKE_CASE				=					type_vocab_size
				__SCREAMING_SNAKE_CASE				=					initializer_range
				__SCREAMING_SNAKE_CASE				=					layer_norm_eps
				__SCREAMING_SNAKE_CASE				=					share_encoders
				__SCREAMING_SNAKE_CASE				=					projection_dim










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 tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



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





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
