





"""simple docstring"""






from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A 			=  [
    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'''
    ''' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'''
    ''' depression\" German airline confirms it knew of Andreas Lubitz\'s depression years before he took control.''',
    '''The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal'''
    ''' accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC\'s'''
    ''' founding Rome Statute in January. Israel and the United States opposed the Palestinians\' efforts to join the'''
    ''' body.''',
    '''Amnesty International releases its annual report on the death penalty. The report catalogs the use of'''
    ''' state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the'''
    ''' world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital'''
    ''' punishment.''',
]

A 			=  [
    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'''
    ''' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'''
    ''' had informed his Lufthansa training school of an episode of severe depression, airline says .''',
    '''Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June .'''
    ''' Israel and the United States opposed the move, which could open the door to war crimes investigations against'''
    ''' Israelis .''',
    '''Amnesty\'s annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to'''
    ''' death . Organization claims that governments around the world are using the threat of terrorism to advance'''
    ''' executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death'''
    ''' sentences up by 28% .''',
]

def     __A      (			)		->		Union[str, Any]:
				__a :						str			       =  calculate_rouge(a_   ,       a_   ,       bootstrap_aggregation=a_   ,       rouge_keys=['''rouge2''', '''rougeL'''])
				assert isinstance(a_   ,       a_)
				__a :						Any			       =  calculate_rouge(a_   ,       a_   ,       bootstrap_aggregation=a_   ,       rouge_keys=['''rouge2'''])
				assert (
				    pd.DataFrame(no_aggregation['''rouge2''']).fmeasure.mean()
				    == pd.DataFrame(no_aggregation_just_ra['''rouge2''']).fmeasure.mean()
				)

def     __A      (			)		->		str:
				__a :						str			       =  '''rougeLsum'''
				__a :						int			       =  calculate_rouge(a_   ,       a_   ,       newline_sep=a_   ,       rouge_keys=[k])[k]
				__a :						List[Any]			       =  calculate_rouge(a_   ,       a_   ,       newline_sep=a_   ,       rouge_keys=[k])[k]
				assert score > score_no_sep

def     __A      (			)		->		List[str]:
				__a :						Optional[Any]			       =  ['''rouge1''', '''rouge2''', '''rougeL''']
				__a :						Any			       =  calculate_rouge(a_   ,       a_   ,       newline_sep=a_   ,       rouge_keys=a_)
				__a :						List[Any]			       =  calculate_rouge(a_   ,       a_   ,       newline_sep=a_   ,       rouge_keys=a_)
				assert score_sep == score_no_sep

def     __A      (			)		->		Any:
				__a :						Union[str, Any]			       =  [
				    '''Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.''',
				    '''Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .''',
				]
				__a :						Tuple			       =  [
				    '''Margot Frank, died in 1945, a month earlier than previously thought.''',
				    '''Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'''
				    ''' the final seconds on board Flight 9525.''',
				]
				assert calculate_rouge(a_   ,       a_   ,       newline_sep=a_) == calculate_rouge(a_   ,       a_   ,       newline_sep=a_)

def     __A      (			)		->		List[Any]:
				__a :						Dict			       =  [
				    '''" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '''
				]
				__a :						List[str]			       =  [
				    ''' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'''
				]

				__a :						List[Any]			       =  calculate_rouge(a_   ,       a_   ,       rouge_keys=['''rougeLsum''']   ,       newline_sep=a_)['''rougeLsum''']
				__a :						Optional[int]			       =  calculate_rouge(a_   ,       a_   ,       rouge_keys=['''rougeLsum'''])['''rougeLsum''']
				assert new_score > prev_score






def     __A      (			)		->		Tuple:
				__a :						Dict			       =  Path('''examples/seq2seq/test_data/wmt_en_ro''')
				__a :						Dict			       =  calculate_rouge_path(data_dir.joinpath('''test.source''')   ,       data_dir.joinpath('''test.target'''))
				assert isinstance(a_   ,       a_)
				__a :						Any			       =  calculate_rouge_path(
				    data_dir.joinpath('''test.source''')   ,       data_dir.joinpath('''test.target''')   ,       bootstrap_aggregation=a_)
				assert isinstance(a_   ,       a_)






"""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 				__lowercase			(			tf.keras.Model							):







							'''simple docstring'''




							def __init__(						self	,			_UpperCAmelCase     ):
											super().__init__()
											__a :						Any			       =  tokenizer
											__a :						Optional[Any]			       =  AutoConfig.from_pretrained(_UpperCAmelCase     )
											__a :						str			       =  TFAutoModel.from_config(_UpperCAmelCase     )







							def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
											__a :						Any			       =  self.tokenizer(_UpperCAmelCase     )
											__a :						int			       =  self.bert(**_UpperCAmelCase     )
											return out["pooler_output"]




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







					'''simple docstring'''




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

									__a :						Any			       =  [
									    BertTokenizer.from_pretrained(_UpperCAmelCase     ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									__a :						Union[str, 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     )

									__a :						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''',
									]
									__a :						Any			       =  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):
																	__a :						List[Any]			       =  tokenizer(_UpperCAmelCase	,			return_tensors='''tf'''	,			padding='''longest'''     )
																	__a :						List[str]			       =  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  _lowerCamelCase		(						self     ):
									for tf_tokenizer in self.tf_tokenizers:
													__a :						Dict			       =  tf_tokenizer(self.paired_sentences     )
													__a :						str			       =  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:
													__a :						Tuple			       =  tf.function(_UpperCAmelCase     )
													for test_inputs in (self.test_sentences, self.paired_sentences):
																	__a :						List[str]			       =  tf.constant(_UpperCAmelCase     )
																	__a :						Tuple			       =  compiled_tokenizer(_UpperCAmelCase     )
																	__a :						Union[str, Any]			       =  tf_tokenizer(_UpperCAmelCase     )

																	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:
													__a :						Dict			       =  ModelToSave(tokenizer=_UpperCAmelCase     )
													__a :						Tuple			       =  tf.convert_to_tensor(self.test_sentences     )
													__a :						List[str]			       =  model(_UpperCAmelCase     )  # Build model with some sample inputs
													with TemporaryDirectory() as tempdir:
																	__a :						Tuple			       =  Path(_UpperCAmelCase     ) / '''saved.model'''
																	model.save(_UpperCAmelCase     )
																	__a :						Tuple			       =  tf.keras.models.load_model(_UpperCAmelCase     )
													__a :						int			       =  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     )