






'''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


__lowerCamelCase   :					Union[str, Any]   							=					['''bert-base-uncased''', '''bert-base-cased''']
__lowerCamelCase   :					Optional[int]   							=					'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():







		class    A_	(tf.keras.Model							):
									"""simple docstring"""




									def __init__(		self			:int       ,							lowerCAmelCase__			:Optional[int]				)				->  Dict:


												'''simple docstring'''




												super().__init__()
												snake_case_				:					int   =       tokenizer
												snake_case_				:					Union[str, Any]   =       AutoConfig.from_pretrained(lowerCAmelCase__				)
												snake_case_				:					List[str]   =       TFAutoModel.from_config(lowerCAmelCase__				)






									def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:str				)				->  str:


												'''simple docstring'''




												snake_case_				:					Dict   =       self.tokenizer(lowerCAmelCase__				)
												snake_case_				:					Tuple   =       self.bert(**lowerCAmelCase__				)
												return out["pooler_output"]







@require_tf
@require_tensorflow_text
class    A_	(unittest.TestCase							):
							"""simple docstring"""




							def 							_A	(		self			:Any				)				->  List[str]:


										'''simple docstring'''




										super().setUp()

										snake_case_				:					str   =       [
										    BertTokenizer.from_pretrained(lowerCAmelCase__				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
										]  # repeat for when fast_bert_tokenizer=false
										snake_case_				:					Dict   =       [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				)

										snake_case_				:					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",
										]
										snake_case_				:					List[Any]   =       list(zip(self.test_sentences       ,							self.test_sentences[::-1]				)				)

							def 							_A	(		self			:Tuple				)				->  Tuple:


										'''simple docstring'''




										for tokenizer, tf_tokenizer in zip(self.tokenizers       ,							self.tf_tokenizers				):
													for test_inputs in (self.test_sentences, self.paired_sentences):
																snake_case_				:					List[Any]   =       tokenizer(lowerCAmelCase__       ,							return_tensors="tf"       ,							padding="longest"				)
																snake_case_				:					Dict   =       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			:Optional[int]				)				->  List[Any]:


										'''simple docstring'''




										for tf_tokenizer in self.tf_tokenizers:
													snake_case_				:					Optional[Any]   =       tf_tokenizer(self.paired_sentences				)
													snake_case_				:					Union[str, 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			:Union[str, Any]				)				->  int:


										'''simple docstring'''




										for tf_tokenizer in self.tf_tokenizers:
													snake_case_				:					Any   =       tf.function(lowerCAmelCase__				)
													for test_inputs in (self.test_sentences, self.paired_sentences):
																snake_case_				:					Optional[Any]   =       tf.constant(lowerCAmelCase__				)
																snake_case_				:					List[Any]   =       compiled_tokenizer(lowerCAmelCase__				)
																snake_case_				:					List[str]   =       tf_tokenizer(lowerCAmelCase__				)

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






							@slow
							def 							_A	(		self			:int				)				->  Optional[int]:


										'''simple docstring'''




										for tf_tokenizer in self.tf_tokenizers:
													snake_case_				:					Optional[int]   =       ModelToSave(tokenizer=lowerCAmelCase__				)
													snake_case_				:					str   =       tf.convert_to_tensor(self.test_sentences				)
													snake_case_				:					Dict   =       model(lowerCAmelCase__				)  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																snake_case_				:					Optional[int]   =       Path(lowerCAmelCase__				) / "saved.model"
																model.save(lowerCAmelCase__				)
																snake_case_				:					Dict   =       tf.keras.models.load_model(lowerCAmelCase__				)
													snake_case_				:					str   =       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				)












'''simple docstring'''




import fire

from utils import calculate_rouge, save_json

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=None     ,**__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       [x.strip() for x in open(__magic_name__						).readlines()]
			snake_case_				:					Optional[int]   =       [x.strip() for x in open(__magic_name__						).readlines()][: len(__magic_name__						)]
			snake_case_				:					List[Any]   =       calculate_rouge(__magic_name__     ,__magic_name__     ,**__magic_name__						)
			if save_path is not None:
						save_json(__magic_name__     ,__magic_name__     ,indent=__magic_name__						)
			return metrics  # these print nicely


if __name__ == "__main__":
		fire.Fire(calculate_rouge_path)




