
"""simple docstring"""


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


A_    							=					logging.get_logger(__name__)

A_    							=					{
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Dict          =    'realm'




				def __init__(				self							,						UpperCAmelCase=3_0522							,						UpperCAmelCase=768							,						UpperCAmelCase=128							,						UpperCAmelCase=12							,						UpperCAmelCase=12							,						UpperCAmelCase=8							,						UpperCAmelCase=3072							,						UpperCAmelCase="gelu_new"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=512							,						UpperCAmelCase=2							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=1e-1_2							,						UpperCAmelCase=256							,						UpperCAmelCase=10							,						UpperCAmelCase=1e-3							,						UpperCAmelCase=5							,						UpperCAmelCase=320							,						UpperCAmelCase=1335_3718							,						UpperCAmelCase=5000							,						UpperCAmelCase=1							,						UpperCAmelCase=0							,						UpperCAmelCase=2							,						**UpperCAmelCase							,						):
							super().__init__(pad_token_id=UpperCAmelCase							,						bos_token_id=UpperCAmelCase							,						eos_token_id=UpperCAmelCase							,						**UpperCAmelCase   )

							# Common config
							lowerCamelCase_					=  vocab_size
							lowerCamelCase_					=  max_position_embeddings
							lowerCamelCase_					=  hidden_size
							lowerCamelCase_					=  retriever_proj_size
							lowerCamelCase_					=  num_hidden_layers
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  num_candidates
							lowerCamelCase_					=  intermediate_size
							lowerCamelCase_					=  hidden_act
							lowerCamelCase_					=  hidden_dropout_prob
							lowerCamelCase_					=  attention_probs_dropout_prob
							lowerCamelCase_					=  initializer_range
							lowerCamelCase_					=  type_vocab_size
							lowerCamelCase_					=  layer_norm_eps

							# Reader config
							lowerCamelCase_					=  span_hidden_size
							lowerCamelCase_					=  max_span_width
							lowerCamelCase_					=  reader_layer_norm_eps
							lowerCamelCase_					=  reader_beam_size
							lowerCamelCase_					=  reader_seq_len

							# Retrieval config
							lowerCamelCase_					=  num_block_records
							lowerCamelCase_					=  searcher_beam_size





"""simple docstring"""




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 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			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     )     )