






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


UpperCamelCase								=		["bert-base-uncased", "bert-base-cased"]
UpperCamelCase								=		"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():

							class 				lowerCAmelCase_	(		tf.keras.Model			):


											def __init__(					self ,						_lowerCAmelCase      ):
																		super().__init__()
																		_lowercase							:   Optional[int] 							=   tokenizer
																		_lowercase							:   Dict 							=   AutoConfig.from_pretrained(_lowerCAmelCase      )
																		_lowercase							:   Any 							=   TFAutoModel.from_config(_lowerCAmelCase      )


											def 						__a					(					self ,						_lowerCAmelCase      ):
																		_lowercase							:   int 							=   self.tokenizer(_lowerCAmelCase      )
																		_lowercase							:   int 							=   self.bert(**_lowerCAmelCase      )
																		return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 				lowerCAmelCase_	(		unittest.TestCase			):


				def 						__a					(					self      ):
											super().setUp()

											_lowercase							:   int 							=   [
											    BertTokenizer.from_pretrained(_lowerCAmelCase      ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_lowercase							:   Optional[int] 							=   [TFBertTokenizer.from_pretrained(_lowerCAmelCase      ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(_lowerCAmelCase ,						use_fast_bert_tokenizer=_lowerCAmelCase      )
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

											_lowercase							:   Any 							=   [
											    'This is a straightforward English test sentence.',
											    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
											    'Now we\'re going to add some Chinese: 一 二 三 一二三',
											    'And some much more rare Chinese: 齉 堃 齉堃',
											    'Je vais aussi écrire en français pour tester les accents',
											    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
											]
											_lowercase							:   Union[str, Any] 							=   list(zip(self.test_sentences ,						self.test_sentences[::-1]      )      )


				def 						__a					(					self      ):
											for tokenizer, tf_tokenizer in zip(self.tokenizers ,						self.tf_tokenizers      ):
																		for test_inputs in (self.test_sentences, self.paired_sentences):
																									_lowercase							:   str 							=   tokenizer(_lowerCAmelCase ,						return_tensors='tf' ,						padding='longest'      )
																									_lowercase							:   int 							=   tf_tokenizer(_lowerCAmelCase      )

																									for key in python_outputs.keys():
																																self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape      )      )
																																self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,						tf.intaa      ) == tf_outputs[key]      )      )


				@slow
				def 						__a					(					self      ):
											for tf_tokenizer in self.tf_tokenizers:
																		_lowercase							:   Union[str, Any] 							=   tf_tokenizer(self.paired_sentences      )
																		_lowercase							:   Any 							=   tf_tokenizer(
																		    text=[sentence[0] for sentence in self.paired_sentences] ,						text_pair=[sentence[1] for sentence in self.paired_sentences] ,						)
																		for key in merged_outputs.keys():
																									self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,						tf.intaa      ) == separated_outputs[key]      )      )


				@slow
				def 						__a					(					self      ):
											for tf_tokenizer in self.tf_tokenizers:
																		_lowercase							:   Dict 							=   tf.function(_lowerCAmelCase      )
																		for test_inputs in (self.test_sentences, self.paired_sentences):
																									_lowercase							:   List[str] 							=   tf.constant(_lowerCAmelCase      )
																									_lowercase							:   Union[str, Any] 							=   compiled_tokenizer(_lowerCAmelCase      )
																									_lowercase							:   Any 							=   tf_tokenizer(_lowerCAmelCase      )

																									for key in eager_outputs.keys():
																																self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]      )      )


				@slow
				def 						__a					(					self      ):
											for tf_tokenizer in self.tf_tokenizers:
																		_lowercase							:   Dict 							=   ModelToSave(tokenizer=_lowerCAmelCase      )
																		_lowercase							:   Any 							=   tf.convert_to_tensor(self.test_sentences      )
																		_lowercase							:   List[Any] 							=   model(_lowerCAmelCase      )  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																									_lowercase							:   int 							=   Path(_lowerCAmelCase      ) / 'saved.model'
																									model.save(_lowerCAmelCase      )
																									_lowercase							:   Any 							=   tf.keras.models.load_model(_lowerCAmelCase      )
																		_lowercase							:   List[Any] 							=   loaded_model(_lowerCAmelCase      )
																		# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																		self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output      )      ) ,						1E-5      )






from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """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     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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

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






class     UpperCAmelCase						(		A_					):



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


          '''simple docstring'''


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





