




'''simple docstring'''



import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Tuple										=      FunnelTokenizer
       __lowercase	:   int										=      FunnelTokenizerFast
       __lowercase	:   Optional[int]										=      True
       __lowercase	:   Union[str, Any]										=      True


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	[
                '''<unk>''',
                '''<cls>''',
                '''<sep>''',
                '''want''',
                '''##want''',
                '''##ed''',
                '''wa''',
                '''un''',
                '''runn''',
                '''##ing''',
                ''',''',
                '''low''',
                '''lowest''',
            ]
            __snake_case            =	os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['''vocab_file''']	)
            with open(self.vocab_file     ,	'''w'''     ,	encoding='''utf-8'''	) as vocab_writer:
                 vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Any:



            '''simple docstring'''




            return FunnelTokenizer.from_pretrained(self.tmpdirname     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	Any:



            '''simple docstring'''




            return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




            __snake_case            =	'''UNwant\u00E9d,running'''
            __snake_case            =	'''unwanted, running'''
            return input_text, output_text


       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	self.tokenizer_class(self.vocab_file	)

            __snake_case            =	tokenizer.tokenize('''UNwant\u00E9d,running'''	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']	)
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE	)     ,	[7, 4, 5, 10, 8, 9]	)




       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizers(do_lower_case=__SCREAMING_SNAKE_CASE	)
            for tokenizer in tokenizers:
                 __snake_case            =	tokenizer('''UNwant\u00E9d,running'''	)
                 __snake_case            =	len(inputs['''input_ids''']	) - 1
                 self.assertListEqual(inputs['''token_type_ids''']     ,	[2] + [0] * sentence_len	)

                 __snake_case            =	tokenizer('''UNwant\u00E9d,running'''     ,	'''UNwant\u00E9d,running'''	)
                 self.assertListEqual(inputs['''token_type_ids''']     ,	[2] + [0] * sentence_len + [1] * sentence_len	)













"""simple docstring"""






import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class 					__UpperCAmelCase							(						_UpperCamelCase  ):
				# to overwrite at feature extractactor specific tests
				__lowerCamelCase				:	int				     =				None
				__lowerCamelCase				:	Tuple				     =				None
				@property
				def       UpperCAmelCase      (		self    :			Optional[int]    )	->	Optional[int]:



							'''simple docstring'''



							return self.feat_extract_tester.prepare_feat_extract_dict()
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Dict:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							self.assertTrue(hasattr(a_	, "feature_size"    )    )
							self.assertTrue(hasattr(a_	, "sampling_rate"    )    )
							self.assertTrue(hasattr(a_	, "padding_value"    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	Union[str, Any]:



							'''simple docstring'''



							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Union[str, Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	Tuple		 =			BatchFeature({input_name: speech_inputs}    )

							self.assertTrue(all(len(a_    ) == len(a_    ) for x, y in zip(a_	, processed_features[input_name]    )    )    )

							a__  :	Any		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="np"    )

							a__  :	Union[str, Any]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[Any]		 =			batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
				@require_torch
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	List[Any]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Any		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="pt"    )

							a__  :	Optional[int]		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	Any		 =			batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
				@require_tf
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common(equal_length=a_    )
							a__  :	Optional[int]		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	str		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}	, tensor_type="tf"    )

							a__  :	Tuple		 =			processed_features[input_name]

							if len(batch_features_input.shape    ) < 3:
										a__  :	List[str]		 =			batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]    ), self.feat_extract_tester.feature_size)    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[Any]=False    )	->	List[Any]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			int    ):
										a__  :	Any		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

							def _inputs_are_equal(a_    :			List[Any]	, a_    :			str    ):
										if len(a_    ) != len(a_    ):
													return False

										for input_slice_a, input_slice_a in zip(a_	, a_    ):
													if not np.allclose(np.asarray(a_    )	, np.asarray(a_    )	, atol=1E-3    ):
																return False
										return True

							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	int		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	List[str]		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	Optional[int]		 =			self.feat_extract_tester.seq_length_diff
							a__  :	Optional[Any]		 =			self.feat_extract_tester.max_seq_length + pad_diff
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.min_seq_length
							a__  :	Tuple		 =			self.feat_extract_tester.batch_size
							a__  :	str		 =			self.feat_extract_tester.feature_size

							# test padding for List[int] + numpy
							a__  :	Any		 =			feat_extract.pad(a_	, padding=a_    )
							a__  :	List[str]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	Optional[Any]		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[-1]    )    )
							a__  :	Any		 =			input_a[input_name]

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							# max_length parameter has to be provided when setting `padding="max_length"`
							with self.assertRaises(a_    ):
										feat_extract.pad(a_	, padding="max_length"    )[input_name]

							a__  :	Tuple		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, return_tensors="np"    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							self.assertFalse(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )
							self.assertTrue(len(input_a[0]    ) == pad_min_length    )
							self.assertTrue(len(input_a[1]    ) == pad_min_length + pad_diff    )
							self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]    ))    )
							self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)    )

							if feature_size > 1:
										self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size    )

							# test padding for `pad_to_multiple_of` for List[int] + numpy
							a__  :	Optional[int]		 =			feat_extract.pad(a_	, pad_to_multiple_of=10    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(a_	, padding="longest"	, pad_to_multiple_of=10    )
							a__  :	Dict		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_    )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, pad_to_multiple_of=10	, max_length=a_	, return_tensors="np"	, )
							a__  :	Optional[int]		 =			input_a[input_name]

							self.assertTrue(all(len(a_    ) % 10 == 0 for x in input_a    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )

							a__  :	List[str]		 =			pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
							self.assertTrue(all(len(a_    ) == expected_mult_pad_length for x in input_a    )    )
							self.assertEqual(input_a.shape[:2]	, (batch_size, expected_mult_pad_length)    )

							if feature_size > 1:
										self.assertTrue(input_a.shape[2] == feature_size    )

							# Check padding value is correct
							a__  :	Optional[int]		 =			(np.ones(self.feat_extract_tester.feature_size    ) * feat_extract.padding_value).sum()
							self.assertTrue(
							    abs(np.asarray(input_a[0]    )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    )
							    < 1E-3    )
							self.assertTrue(
							    abs(
							        np.asarray(input_a[1]    )[pad_min_length + pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)    )
							    < 1E-3    )
							self.assertTrue(
							    abs(
							        np.asarray(input_a[2]    )[pad_min_length + 2 * pad_diff :].sum()
							        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)    )
							    < 1E-3    )
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)    ) < 1E-3    )
							self.assertTrue(
							    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)    )
							    < 1E-3    )
				def       UpperCAmelCase      (		self    :			Dict	, a_    :			Optional[int]=False    )	->	List[str]:



							'''simple docstring'''



							def _inputs_have_equal_length(a_    :			List[Any]    ):
										a__  :	List[str]		 =			len(input[0]    )
										for input_slice in input[1:]:
													if len(a_    ) != length:
																return False
										return True

							def _inputs_are_equal(a_    :			Optional[int]	, a_    :			List[Any]    ):
										if len(a_    ) != len(a_    ):
													return False

										for input_slice_a, input_slice_a in zip(a_	, a_    ):
													if not np.allclose(np.asarray(a_    )	, np.asarray(a_    )	, atol=1E-3    ):
																return False
										return True

							a__  :	Dict		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Union[str, Any]		 =			self.feat_extract_tester.prepare_inputs_for_common(numpify=a_    )
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							# truncate to smallest
							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, truncation=a_    )
							a__  :	Optional[Any]		 =			input_a[input_name]

							a__  :	int		 =			feat_extract.pad(a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )    )
							a__  :	List[Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )

							# truncate to smallest with np
							a__  :	Union[str, Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"	, truncation=a_	, )
							a__  :	Tuple		 =			input_a[input_name]

							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, return_tensors="np"    )
							a__  :	Union[str, Any]		 =			input_a[input_name]

							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(input_a.shape[1] == len(speech_inputs[0]    )    )

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(a_    )    )

							# truncate to middle
							a__  :	str		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_	, return_tensors="np"	, )
							a__  :	List[Any]		 =			input_a[input_name]

							a__  :	List[str]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, truncation=a_    )
							a__  :	str		 =			input_a[input_name]

							a__  :	Any		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[1]    )	, return_tensors="np"    )
							a__  :	Optional[int]		 =			input_a[input_name]

							self.assertTrue(input_a.shape[1] == len(speech_inputs[1]    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertTrue(_inputs_are_equal(a_	, a_    )    )

							# since truncation forces padding to be smaller than longest input
							# function can't return `np.ndarray`, but has to return list
							self.assertFalse(_inputs_have_equal_length(a_    )    )
							self.assertTrue(len(input_a[-1]    ) == len(speech_inputs[-1]    )    )

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(a_    ):
										feat_extract.pad(a_	, truncation=a_    )[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(a_    ):
										feat_extract.pad(a_	, padding="longest"	, truncation=a_    )[input_name]

							# padding has to be max_length when setting `truncation=True`
							with self.assertRaises(a_    ):
										feat_extract.pad(a_	, padding="longest"	, truncation=a_    )[input_name]

							# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
							with self.assertRaises(a_    ):
										feat_extract.pad(a_	, padding="max_length"	, truncation=a_    )[input_name]

							# test truncation for `pad_to_multiple_of` for List[int] + numpy
							a__  :	Tuple		 =			12
							a__  :	Dict		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, truncation=a_	, )
							a__  :	Optional[int]		 =			input_a[input_name]

							a__  :	List[Any]		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=len(speech_inputs[0]    )	, pad_to_multiple_of=a_	, )
							a__  :	str		 =			input_a[input_name]

							# retrieve expected_length as multiple of pad_to_multiple_of
							a__  :	Dict		 =			len(speech_inputs[0]    )
							if expected_length % pad_to_multiple_of != 0:
										a__  :	Tuple		 =			((len(speech_inputs[0]    ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

							self.assertTrue(len(input_a[0]    ) == expected_length    )
							self.assertTrue(_inputs_have_equal_length(a_    )    )
							self.assertFalse(_inputs_have_equal_length(a_    )    )
				def       UpperCAmelCase      (		self    :			List[str]    )	->	List[str]:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			Tuple    )	->	Tuple:



							'''simple docstring'''



							self._check_padding(numpify=a_    )
				def       UpperCAmelCase      (		self    :			int    )	->	Optional[Any]:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				def       UpperCAmelCase      (		self    :			str    )	->	int:



							'''simple docstring'''



							self._check_truncation(numpify=a_    )
				@require_torch
				def       UpperCAmelCase      (		self    :			List[Any]    )	->	Tuple:



							'''simple docstring'''



							a__  :	Tuple		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Tuple		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	int		 =			feat_extract.model_input_names[0]

							a__  :	str		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	int		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	str		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="pt"    )[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_pt.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )
				@require_tf
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	str:



							'''simple docstring'''



							a__  :	int		 =			self.feature_extraction_class(**self.feat_extract_dict    )
							a__  :	Optional[int]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[Any]		 =			feat_extract.model_input_names[0]

							a__  :	Dict		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	List[Any]		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )[input_name]
							a__  :	List[str]		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="tf"    )[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa    ).sum() - input_tf.numpy().astype(np.floataa    ).sum()    ) < 1E-2    )
				def       UpperCAmelCase      (		self    :			Optional[Any]    )	->	List[str]:



							'''simple docstring'''



							a__  :	Union[str, Any]		 =			self.feat_extract_dict
							a__  :	Dict		 =			True
							a__  :	Dict		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	List[Any]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	List[str]		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )

							a__  :	Tuple		 =			feat_extract.pad(a_	, padding="longest"	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							self.assertListEqual(list(processed.attention_mask.shape    )	, list(processed[input_name].shape[:2]    )    )
							self.assertListEqual(processed.attention_mask.sum(-1    ).tolist()	, a_    )






				def       UpperCAmelCase      (		self    :			List[Any]    )	->	List[Any]:



							'''simple docstring'''



							a__  :	List[str]		 =			self.feat_extract_dict
							a__  :	Any		 =			True
							a__  :	Optional[Any]		 =			self.feature_extraction_class(**a_    )
							a__  :	List[Any]		 =			self.feat_extract_tester.prepare_inputs_for_common()
							a__  :	Optional[int]		 =			[len(a_    ) for x in speech_inputs]
							a__  :	Tuple		 =			feat_extract.model_input_names[0]

							a__  :	int		 =			BatchFeature({input_name: speech_inputs}    )
							a__  :	Tuple		 =			min(a_    )

							a__  :	int		 =			feat_extract.pad(
							    a_	, padding="max_length"	, max_length=a_	, truncation=a_	, return_tensors="np"    )
							self.assertIn("attention_mask"	, a_    )
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape    )	, [processed_pad[input_name].shape[0], max_length]    )
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1    ).tolist()	, [max_length for x in speech_inputs]    )