




"""simple docstring"""





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


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

if is_tf_available():



					class      a__				(	tf.keras.Model   ):


								def __init__(      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[int]):


											"""simple docstring"""




											super().__init__()
											__UpperCAmelCase	: Union[str, Any]		=					tokenizer
											__UpperCAmelCase	: List[Any]		=					AutoConfig.from_pretrained(UpperCamelCase_)
											__UpperCAmelCase	: Tuple		=					TFAutoModel.from_config(UpperCamelCase_)


								def       a_     (      self      :    int      ,  UpperCamelCase_      :    str):


											"""simple docstring"""




											__UpperCAmelCase	: Tuple		=					self.tokenizer(UpperCamelCase_)
											__UpperCAmelCase	: Optional[int]		=					self.bert(**UpperCamelCase_)
											return out["pooler_output"]



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


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						super().setUp()

						__UpperCAmelCase	: str		=					[
						    BertTokenizer.from_pretrained(UpperCamelCase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
						]  # repeat for when fast_bert_tokenizer=false
						__UpperCAmelCase	: List[Any]		=					[TFBertTokenizer.from_pretrained(UpperCamelCase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
						    TFBertTokenizer.from_pretrained(UpperCamelCase_      ,  use_fast_bert_tokenizer=UpperCamelCase_)
						    for checkpoint in TOKENIZER_CHECKPOINTS
						]
						assert len(self.tokenizers) == len(self.tf_tokenizers)

						__UpperCAmelCase	: Tuple		=					[
						    "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	: Union[str, Any]		=					list(zip(self.test_sentences      ,  self.test_sentences[::-1]))


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						for tokenizer, tf_tokenizer in zip(self.tokenizers      ,  self.tf_tokenizers):
									for test_inputs in (self.test_sentences, self.paired_sentences):
												__UpperCAmelCase	: Dict		=					tokenizer(UpperCamelCase_      ,  return_tensors="tf"      ,  padding="longest")
												__UpperCAmelCase	: List[Any]		=					tf_tokenizer(UpperCamelCase_)

												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      :    Tuple):


						"""simple docstring"""




						for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase	: Optional[int]		=					tf_tokenizer(self.paired_sentences)
									__UpperCAmelCase	: int		=					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      :    Dict):


						"""simple docstring"""




						for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase	: Tuple		=					tf.function(UpperCamelCase_)
									for test_inputs in (self.test_sentences, self.paired_sentences):
												__UpperCAmelCase	: Tuple		=					tf.constant(UpperCamelCase_)
												__UpperCAmelCase	: Union[str, Any]		=					compiled_tokenizer(UpperCamelCase_)
												__UpperCAmelCase	: str		=					tf_tokenizer(UpperCamelCase_)

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


			@slow
			def       a_     (      self      :    int):


						"""simple docstring"""




						for tf_tokenizer in self.tf_tokenizers:
									__UpperCAmelCase	: Optional[Any]		=					ModelToSave(tokenizer=UpperCamelCase_)
									__UpperCAmelCase	: Tuple		=					tf.convert_to_tensor(self.test_sentences)
									__UpperCAmelCase	: Tuple		=					model(UpperCamelCase_)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												__UpperCAmelCase	: Tuple		=					Path(UpperCamelCase_) / "saved.model"
												model.save(UpperCamelCase_)
												__UpperCAmelCase	: Optional[int]		=					tf.keras.models.load_model(UpperCamelCase_)
									__UpperCAmelCase	: Union[str, Any]		=					loaded_model(UpperCamelCase_)
									# 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"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


