





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 							__SCREAMING_SNAKE_CASE       (  tf.keras.Model							):






												def __init__(    self	,			__lowerCAmelCase	):
																			super().__init__()
																			UpperCamelCase__					  =      tokenizer
																			UpperCamelCase__					  =      AutoConfig.from_pretrained(lowercase_	)
																			UpperCamelCase__					  =      TFAutoModel.from_config(lowercase_	)







												def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
																			UpperCamelCase__					  =      self.tokenizer(lowercase_	)
																			UpperCamelCase__					  =      self.bert(**lowercase_	)
																			return out["pooler_output"]

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






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

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

														UpperCamelCase__					  =      [
														    """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""",
														]
														UpperCamelCase__					  =      list(zip(self.test_sentences	,			self.test_sentences[::-1]	)	)






							def 					_lowerCamelCase		(    self	):


														for tokenizer, tf_tokenizer in zip(self.tokenizers	,			self.tf_tokenizers	):
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												UpperCamelCase__					  =      tokenizer(lowercase_	,			return_tensors="""tf"""	,			padding="""longest"""	)
																												UpperCamelCase__					  =      tf_tokenizer(lowercase_	)

																												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 					_lowerCamelCase		(    self	):


														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      tf_tokenizer(self.paired_sentences	)
																					UpperCamelCase__					  =      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 					_lowerCamelCase		(    self	):


														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      tf.function(lowercase_	)
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												UpperCamelCase__					  =      tf.constant(lowercase_	)
																												UpperCamelCase__					  =      compiled_tokenizer(lowercase_	)
																												UpperCamelCase__					  =      tf_tokenizer(lowercase_	)

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







							@slow
							def 					_lowerCamelCase		(    self	):


														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      ModelToSave(tokenizer=lowercase_	)
																					UpperCamelCase__					  =      tf.convert_to_tensor(self.test_sentences	)
																					UpperCamelCase__					  =      model(lowercase_	)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												UpperCamelCase__					  =      Path(lowercase_	) / """saved.model"""
																												model.save(lowercase_	)
																												UpperCamelCase__					  =      tf.keras.models.load_model(lowercase_	)
																					UpperCamelCase__					  =      loaded_model(lowercase_	)
																					# 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	)





'''simple docstring'''


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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


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

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

