


'''simple docstring'''
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   lowercase      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							GPTaTokenizer
		_a       					=							GPTaTokenizerFast
		_a       					=							True
		_a       					=							{'add_prefix_space': True}
		_a       					=							False




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCamelCase__  :List[str]				   =					[
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''\u0120''',
							    '''\u0120l''',
							    '''\u0120n''',
							    '''\u0120lo''',
							    '''\u0120low''',
							    '''er''',
							    '''\u0120lowest''',
							    '''\u0120newer''',
							    '''\u0120wider''',
							    '''<unk>''',
							    '''<|endoftext|>''',
							]
							UpperCamelCase__  :Any				   =					dict(zip(UpperCamelCase_       ,  range(len(UpperCamelCase_				)				)				)				)
							UpperCamelCase__  :Tuple				   =					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
							UpperCamelCase__  :Union[str, Any]				   =					{'''unk_token''': '''<unk>'''}

							UpperCamelCase__  :Optional[Any]				   =					os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''vocab_file''']				)
							UpperCamelCase__  :str				   =					os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''merges_file''']				)
							with open(self.vocab_file       ,  '''w'''       ,  encoding='''utf-8'''				) as fp:
												fp.write(json.dumps(UpperCamelCase_				) + '''\n'''				)
							with open(self.merges_file       ,  '''w'''       ,  encoding='''utf-8'''				) as fp:
												fp.write('''\n'''.join(UpperCamelCase_				)				)




		def        lowerCAmelCase__							(						self       ,  **UpperCamelCase_				):

							'''simple docstring'''


							kwargs.update(self.special_tokens_map				)
							return GPTaTokenizer.from_pretrained(self.tmpdirname       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  **UpperCamelCase_				):

							'''simple docstring'''


							kwargs.update(self.special_tokens_map				)
							return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					'''lower newer'''
							UpperCamelCase__  :Tuple				   =					'''lower newer'''
							return input_text, output_text




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					GPTaTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map				)
							UpperCamelCase__  :Dict				   =					'''lower newer'''
							UpperCamelCase__  :Any				   =					['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
							UpperCamelCase__  :Union[str, Any]				   =					tokenizer.tokenize(UpperCamelCase_       ,  add_prefix_space=UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Dict				   =					tokens + [tokenizer.unk_token]
							UpperCamelCase__  :Any				   =					[14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,  UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if not self.test_rust_tokenizer:
												return

							UpperCamelCase__  :str				   =					self.get_tokenizer()
							UpperCamelCase__  :Tuple				   =					self.get_rust_tokenizer(add_prefix_space=UpperCamelCase_				)

							UpperCamelCase__  :Union[str, Any]				   =					'''lower newer'''

							# Testing tokenization
							UpperCamelCase__  :str				   =					tokenizer.tokenize(UpperCamelCase_       ,  add_prefix_space=UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					rust_tokenizer.tokenize(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							# Testing conversion to ids without special tokens
							UpperCamelCase__  :Optional[int]				   =					tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_       ,  add_prefix_space=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					rust_tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							# Testing conversion to ids with special tokens
							UpperCamelCase__  :Any				   =					self.get_rust_tokenizer(add_prefix_space=UpperCamelCase_				)
							UpperCamelCase__  :Union[str, Any]				   =					tokenizer.encode(UpperCamelCase_       ,  add_prefix_space=UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					rust_tokenizer.encode(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							# Testing the unknown token
							UpperCamelCase__  :Union[str, Any]				   =					tokens + [rust_tokenizer.unk_token]
							UpperCamelCase__  :Tuple				   =					[14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,  UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=15				):

							'''simple docstring'''


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
																	UpperCamelCase__  :Optional[Any]				   =					self.rust_tokenizer_class.from_pretrained(UpperCamelCase_       ,  **UpperCamelCase_				)

																	# Simple input
																	UpperCamelCase__  :Tuple				   =					'''This is a simple input'''
																	UpperCamelCase__  :Any				   =					['''This is a simple input 1''', '''This is a simple input 2''']
																	UpperCamelCase__  :Tuple				   =					('''This is a simple input''', '''This is a pair''')
																	UpperCamelCase__  :List[Any]				   =					[
																	    ('''This is a simple input 1''', '''This is a simple input 2'''),
																	    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																	]

																	# Simple input tests
																	self.assertRaises(UpperCamelCase_       ,  tokenizer_r.encode       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''				)

																	# Simple input
																	self.assertRaises(UpperCamelCase_       ,  tokenizer_r.encode_plus       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''				)

																	# Simple input
																	self.assertRaises(
																	    UpperCamelCase_       ,  tokenizer_r.batch_encode_plus       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''       ,  )

																	# Pair input
																	self.assertRaises(UpperCamelCase_       ,  tokenizer_r.encode       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''				)

																	# Pair input
																	self.assertRaises(UpperCamelCase_       ,  tokenizer_r.encode_plus       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''				)

																	# Pair input
																	self.assertRaises(
																	    UpperCamelCase_       ,  tokenizer_r.batch_encode_plus       ,  UpperCamelCase_       ,  max_length=UpperCamelCase_       ,  padding='''max_length'''       ,  )




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					GPTaTokenizer.from_pretrained(self.tmpdirname       ,  pad_token='''<pad>'''				)

							# Simple input
							UpperCamelCase__  :int				   =					'''This is a simple input'''
							UpperCamelCase__  :Tuple				   =					['''This is a simple input looooooooong''', '''This is a simple input''']
							UpperCamelCase__  :Dict				   =					('''This is a simple input''', '''This is a pair''')
							UpperCamelCase__  :Union[str, Any]				   =					[
							    ('''This is a simple input loooooong''', '''This is a simple input'''),
							    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
							]

							UpperCamelCase__  :int				   =					tokenizer.pad_token_id

							UpperCamelCase__  :List[Any]				   =					tokenizer(UpperCamelCase_       ,  padding='''max_length'''       ,  max_length=30       ,  return_tensors='''np'''				)
							UpperCamelCase__  :int				   =					tokenizer(UpperCamelCase_       ,  padding=UpperCamelCase_       ,  truncate=UpperCamelCase_       ,  return_tensors='''np'''				)
							UpperCamelCase__  :int				   =					tokenizer(*UpperCamelCase_       ,  padding='''max_length'''       ,  max_length=60       ,  return_tensors='''np'''				)
							UpperCamelCase__  :Optional[int]				   =					tokenizer(UpperCamelCase_       ,  padding=UpperCamelCase_       ,  truncate=UpperCamelCase_       ,  return_tensors='''np'''				)

							# s
							# test single string max_length padding
							self.assertEqual(out_s['''input_ids'''].shape[-1]       ,  30				)
							self.assertTrue(pad_token_id in out_s['''input_ids''']				)
							self.assertTrue(0 in out_s['''attention_mask''']				)

							# s2
							# test automatic padding
							self.assertEqual(out_sa['''input_ids'''].shape[-1]       ,  33				)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]				)
							self.assertFalse(0 in out_sa['''attention_mask'''][0]				)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]				)
							self.assertTrue(0 in out_sa['''attention_mask'''][1]				)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p['''input_ids'''].shape[-1]       ,  60				)
							self.assertTrue(pad_token_id in out_p['''input_ids''']				)
							self.assertTrue(0 in out_p['''attention_mask''']				)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa['''input_ids'''].shape[-1]       ,  52				)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]				)
							self.assertFalse(0 in out_pa['''attention_mask'''][0]				)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]				)
							self.assertTrue(0 in out_pa['''attention_mask'''][1]				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					'''$$$'''
							UpperCamelCase__  :str				   =					GPTaTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=UpperCamelCase_       ,  add_bos_token=UpperCamelCase_				)

							UpperCamelCase__  :int				   =					'''This is a simple input'''
							UpperCamelCase__  :List[str]				   =					['''This is a simple input 1''', '''This is a simple input 2''']

							UpperCamelCase__  :List[Any]				   =					tokenizer.bos_token_id

							UpperCamelCase__  :Any				   =					tokenizer(UpperCamelCase_				)
							UpperCamelCase__  :List[Any]				   =					tokenizer(UpperCamelCase_				)

							self.assertEqual(out_s.input_ids[0]       ,  UpperCamelCase_				)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

							UpperCamelCase__  :Optional[int]				   =					tokenizer.decode(out_s.input_ids				)
							UpperCamelCase__  :Optional[Any]				   =					tokenizer.batch_decode(out_sa.input_ids				)

							self.assertEqual(decode_s.split()[0]       ,  UpperCamelCase_				)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					[self.get_tokenizer(do_lower_case=UpperCamelCase_       ,  add_bos_token=UpperCamelCase_				)]
							for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''				):
																	UpperCamelCase__  :Optional[int]				   =					'''Encode this.'''
																	UpperCamelCase__  :int				   =					'''This one too please.'''
																	UpperCamelCase__  :Optional[int]				   =					tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
																	encoded_sequence += tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
																	UpperCamelCase__  :Optional[Any]				   =					tokenizer.encode_plus(
																	    UpperCamelCase_       ,  UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_       ,  return_special_tokens_mask=UpperCamelCase_       ,  )
																	UpperCamelCase__  :Tuple				   =					encoded_sequence_dict['''input_ids''']
																	UpperCamelCase__  :Tuple				   =					encoded_sequence_dict['''special_tokens_mask''']
																	self.assertEqual(len(UpperCamelCase_				)       ,  len(UpperCamelCase_				)				)

																	UpperCamelCase__  :Tuple				   =					[
																	    (x if not special_tokens_mask[i] else None) for i, x in enumerate(UpperCamelCase_				)
																	]
																	UpperCamelCase__  :Optional[Any]				   =					[x for x in filtered_sequence if x is not None]
																	self.assertEqual(UpperCamelCase_       ,  UpperCamelCase_				)



@require_tokenizers
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,  from_slow=UpperCamelCase_				)
							UpperCamelCase__  :List[str]				   =					'''A photo of a cat'''

							UpperCamelCase__  :Tuple				   =					tokenizer.encode(
							    UpperCamelCase_       ,  )
							self.assertEqual(UpperCamelCase_       ,  [2, 250, 1345, 9, 10, 4758]				)
							tokenizer.save_pretrained('''test_opt'''				)

							UpperCamelCase__  :Union[str, Any]				   =					AutoTokenizer.from_pretrained('''./test_opt'''				)
							UpperCamelCase__  :Union[str, Any]				   =					tokenizer.encode(
							    UpperCamelCase_       ,  )
							self.assertEqual(UpperCamelCase_       ,  [2, 250, 1345, 9, 10, 4758]				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,  use_slow=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					'''A photo of a cat'''

							UpperCamelCase__  :Dict				   =					tokenizer.encode(
							    UpperCamelCase_       ,  )
							# Same as above
							self.assertEqual(UpperCamelCase_       ,  [2, 250, 1345, 9, 10, 4758]				)




		@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,  from_slow=UpperCamelCase_				)

							UpperCamelCase__  :int				   =					'''bos'''
							UpperCamelCase__  :Union[str, Any]				   =					tokenizer.get_vocab()['''bos''']

							UpperCamelCase__  :Optional[int]				   =					'''A photo of a cat'''
							UpperCamelCase__  :List[Any]				   =					tokenizer.encode(
							    UpperCamelCase_       ,  )
							# We changed the bos token
							self.assertEqual(UpperCamelCase_       ,  [31957, 250, 1345, 9, 10, 4758]				)
							tokenizer.save_pretrained('''./tok'''				)
							UpperCamelCase__  :Optional[int]				   =					AutoTokenizer.from_pretrained('''./tok'''				)
							self.assertTrue(tokenizer.is_fast				)
							UpperCamelCase__  :Dict				   =					tokenizer.encode(
							    UpperCamelCase_       ,  )
							self.assertEqual(UpperCamelCase_       ,  [31957, 250, 1345, 9, 10, 4758]				)



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

							super().__init__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class