


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	):

 '''simple docstring'''

 __snake_case												=  FunnelTokenizer
 __snake_case												=  FunnelTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



 def 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return FunnelTokenizer.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				**_UpperCAmelCase ):
       return FunnelTokenizerFast.from_pretrained(self.tmpdirname		,				**_UpperCAmelCase )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       snake_case_	=				'''UNwant\u00E9d,running'''
       snake_case_	=				'''unwanted, running'''
       return input_text, output_text



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.tokenizer_class(self.vocab_file )

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





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_tokenizers(do_lower_case=_UpperCAmelCase )
       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 )



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  None
 __snake_case												=  None



 @property
 def 						UpperCamelCase__					(     self ):
       return self.feat_extract_tester.prepare_feat_extract_dict()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''feature_size''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''sampling_rate''' ) )
       self.assertTrue(hasattr(_UpperCAmelCase		,				'''padding_value''' ) )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

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

       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''np''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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 ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''pt''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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 ):
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(equal_length=_UpperCAmelCase )
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs}		,				tensor_type='''tf''' )

       snake_case_	=				processed_features[input_name]

       if len(batch_features_input.shape ) < 3:
             snake_case_	=				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		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

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

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				self.feat_extract_tester.seq_length_diff
       snake_case_	=				self.feat_extract_tester.max_seq_length + pad_diff
       snake_case_	=				self.feat_extract_tester.min_seq_length
       snake_case_	=				self.feat_extract_tester.batch_size
       snake_case_	=				self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest''' )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[-1] ) )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

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

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertTrue(_inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ) )
       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
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				pad_to_multiple_of=10 )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				pad_to_multiple_of=10		,				max_length=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

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

       snake_case_	=				pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(_UpperCAmelCase ) == 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
       snake_case_	=				(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		,				_UpperCAmelCase=False ):
       def _inputs_have_equal_length(_UpperCAmelCase ):
             snake_case_	=				len(input[0] )
             for input_slice in input[1:]:
                   if len(_UpperCAmelCase ) != length:
                         return False
             return True

       def _inputs_are_equal(_UpperCAmelCase		,				_UpperCAmelCase ):
             if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
                   return False

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

       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common(numpify=_UpperCAmelCase )
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       # truncate to smallest
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] ) )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )

       # truncate to smallest with np
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np'''		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(_UpperCAmelCase ) )
       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(_UpperCAmelCase ) )

       # truncate to middle
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase		,				return_tensors='''np'''		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				truncation=_UpperCAmelCase )
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[1] )		,				return_tensors='''np''' )
       snake_case_	=				input_a[input_name]

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

       # 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(_UpperCAmelCase ) )
       self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

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

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

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

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

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       snake_case_	=				12
       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=len(speech_inputs[0] )		,				pad_to_multiple_of=_UpperCAmelCase		,				)
       snake_case_	=				input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       snake_case_	=				len(speech_inputs[0] )
       if expected_length % pad_to_multiple_of != 0:
             snake_case_	=				((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(_UpperCAmelCase ) )
       self.assertFalse(_inputs_have_equal_length(_UpperCAmelCase ) )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_padding(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       self._check_truncation(numpify=_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				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 ):
       snake_case_	=				self.feature_extraction_class(**self.feat_extract_dict )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )[input_name]
       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				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 ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )

       snake_case_	=				feat_extract.pad(_UpperCAmelCase		,				padding='''longest'''		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       self.assertListEqual(list(processed.attention_mask.shape )		,				list(processed[input_name].shape[:2] ) )
       self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()		,				_UpperCAmelCase )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.feat_extract_dict
       snake_case_	=				True
       snake_case_	=				self.feature_extraction_class(**_UpperCAmelCase )
       snake_case_	=				self.feat_extract_tester.prepare_inputs_for_common()
       snake_case_	=				[len(_UpperCAmelCase ) for x in speech_inputs]
       snake_case_	=				feat_extract.model_input_names[0]

       snake_case_	=				BatchFeature({input_name: speech_inputs} )
       snake_case_	=				min(_UpperCAmelCase )

       snake_case_	=				feat_extract.pad(
           _UpperCAmelCase		,				padding='''max_length'''		,				max_length=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''np''' )
       self.assertIn('''attention_mask'''		,				_UpperCAmelCase )
       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] )