


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import CLIPImageProcessor, CLIPProcessor







@require_vision
class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       snake_case_	=				tempfile.mkdtemp()

       # fmt: off
       snake_case_	=				['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
       # fmt: on
       snake_case_	=				dict(zip(_UpperCAmelCase		,				range(len(_UpperCAmelCase ) ) ) )
       snake_case_	=				['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
       snake_case_	=				{'''unk_token''': '''<unk>'''}

       snake_case_	=				os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file'''] )
       snake_case_	=				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 ) )

       snake_case_	=				{
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
           '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
       }
       snake_case_	=				os.path.join(self.tmpdirname		,				_UpperCAmelCase )
       with open(self.image_processor_file		,				'''w'''		,				encoding='''utf-8''' ) as fp:
             json.dump(_UpperCAmelCase		,				_UpperCAmelCase )



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



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



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



 def 						UpperCamelCase__					(     self ):
       shutil.rmtree(self.tmpdirname )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				[np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta )]

       snake_case_	=				[Image.fromarray(np.moveaxis(_UpperCAmelCase		,				0		,				-1 ) ) for x in image_inputs]

       return image_inputs



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_tokenizer()
       snake_case_	=				self.get_rust_tokenizer()
       snake_case_	=				self.get_image_processor()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )
       processor_slow.save_pretrained(self.tmpdirname )
       snake_case_	=				CLIPProcessor.from_pretrained(self.tmpdirname		,				use_fast=_UpperCAmelCase )

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )
       processor_fast.save_pretrained(self.tmpdirname )
       snake_case_	=				CLIPProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor_slow.tokenizer.get_vocab()		,				tokenizer_slow.get_vocab() )
       self.assertEqual(processor_fast.tokenizer.get_vocab()		,				tokenizer_fast.get_vocab() )
       self.assertEqual(tokenizer_slow.get_vocab()		,				tokenizer_fast.get_vocab() )
       self.assertIsInstance(processor_slow.tokenizer		,				_UpperCAmelCase )
       self.assertIsInstance(processor_fast.tokenizer		,				_UpperCAmelCase )

       self.assertEqual(processor_slow.image_processor.to_json_string()		,				image_processor.to_json_string() )
       self.assertEqual(processor_fast.image_processor.to_json_string()		,				image_processor.to_json_string() )
       self.assertIsInstance(processor_slow.image_processor		,				_UpperCAmelCase )
       self.assertIsInstance(processor_fast.image_processor		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPProcessor(tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor() )
       processor.save_pretrained(self.tmpdirname )

       snake_case_	=				self.get_tokenizer(bos_token='''(BOS)'''		,				eos_token='''(EOS)''' )
       snake_case_	=				self.get_image_processor(do_normalize=_UpperCAmelCase		,				padding_value=1.0 )

       snake_case_	=				CLIPProcessor.from_pretrained(
           self.tmpdirname		,				bos_token='''(BOS)'''		,				eos_token='''(EOS)'''		,				do_normalize=_UpperCAmelCase		,				padding_value=1.0 )

       self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer_add_kwargs.get_vocab() )
       self.assertIsInstance(processor.tokenizer		,				_UpperCAmelCase )

       self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string() )
       self.assertIsInstance(processor.image_processor		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_image_processor()
       snake_case_	=				self.get_tokenizer()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )

       snake_case_	=				self.prepare_image_inputs()

       snake_case_	=				image_processor(_UpperCAmelCase		,				return_tensors='''np''' )
       snake_case_	=				processor(images=_UpperCAmelCase		,				return_tensors='''np''' )

       for key in input_image_proc.keys():
             self.assertAlmostEqual(input_image_proc[key].sum()		,				input_processor[key].sum()		,				delta=1E-2 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_image_processor()
       snake_case_	=				self.get_tokenizer()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )

       snake_case_	=				'''lower newer'''

       snake_case_	=				processor(text=_UpperCAmelCase )

       snake_case_	=				tokenizer(_UpperCAmelCase )

       for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]		,				encoded_processor[key] )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_image_processor()
       snake_case_	=				self.get_tokenizer()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )

       snake_case_	=				'''lower newer'''
       snake_case_	=				self.prepare_image_inputs()

       snake_case_	=				processor(text=_UpperCAmelCase		,				images=_UpperCAmelCase )

       self.assertListEqual(list(inputs.keys() )		,				['''input_ids''', '''attention_mask''', '''pixel_values'''] )

       # test if it raises when no input is passed
       with pytest.raises(_UpperCAmelCase ):
             processor()



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_image_processor()
       snake_case_	=				self.get_tokenizer()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )

       snake_case_	=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       snake_case_	=				processor.batch_decode(_UpperCAmelCase )
       snake_case_	=				tokenizer.batch_decode(_UpperCAmelCase )

       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.get_image_processor()
       snake_case_	=				self.get_tokenizer()

       snake_case_	=				CLIPProcessor(tokenizer=_UpperCAmelCase		,				image_processor=_UpperCAmelCase )

       snake_case_	=				'''lower newer'''
       snake_case_	=				self.prepare_image_inputs()

       snake_case_	=				processor(text=_UpperCAmelCase		,				images=_UpperCAmelCase )

       self.assertListEqual(list(inputs.keys() )		,				processor.model_input_names )



import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase      =   get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



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

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''</s>'''
       snake_case_	=				1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase )		,				_UpperCAmelCase )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase )		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				list(self.get_tokenizer().get_vocab().keys() )

       self.assertEqual(vocab_keys[0]		,				'''<pad>''' )
       self.assertEqual(vocab_keys[1]		,				'''</s>''' )
       self.assertEqual(vocab_keys[-1]		,				'''v''' )
       self.assertEqual(len(_UpperCAmelCase )		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       self.assertEqual(self.get_tokenizer().vocab_size		,				11_03 )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
           ''' </s> <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       snake_case_	=				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
       snake_case_	=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 9_61_03
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 1_03
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 10_24
       snake_case_	=				'''To ensure a smooth flow of bank resolutions.'''
       snake_case_	=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
       snake_case_	=				tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 1_50, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 10_24)
       assert batch.attention_mask.shape == (2, 10_24)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 @slow
 def 						UpperCamelCase__					(     self ):
       # fmt: off
       snake_case_	=				{'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase		,				model_name='''google/bigbird-pegasus-large-arxiv'''		,				revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,				)







@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  PegasusTokenizer
 __snake_case												=  PegasusTokenizerFast
 __snake_case												=  True
 __snake_case												=  True



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

       # We have a SentencePiece fixture for testing
       snake_case_	=				PegasusTokenizer(_UpperCAmelCase		,				offset=0		,				mask_token_sent=_UpperCAmelCase		,				mask_token='''[MASK]''' )
       tokenizer.save_pretrained(self.tmpdirname )



 @cached_property
 def 						UpperCamelCase__					(     self ):
       return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )



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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       return ("This is a test", "This is a test")



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				self.tokenizer_class.from_pretrained(self.tmpdirname )
       snake_case_	=				(
           '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
           ''' <pad> <pad> <pad>'''
       )
       snake_case_	=				rust_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       snake_case_	=				py_tokenizer([raw_input_str]		,				return_tensors=_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase ).input_ids[0]
       self.assertListEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 @require_torch
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				['''This is going to be way too long.''' * 10_00, '''short example''']
       snake_case_	=				['''not super long but more than 5 tokens''', '''tiny''']
       snake_case_	=				self._large_tokenizer(_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )
       snake_case_	=				self._large_tokenizer(
           text_target=_UpperCAmelCase		,				max_length=5		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				return_tensors='''pt''' )

       assert batch.input_ids.shape == (2, 40_96)
       assert batch.attention_mask.shape == (2, 40_96)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase ) == 2  # input_ids, attention_mask.





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				(
           '''This is an example string that is used to test the original TF implementation against the HF'''
           ''' implementation'''
       )

       snake_case_	=				self._large_tokenizer(_UpperCAmelCase ).input_ids

       self.assertListEqual(
           _UpperCAmelCase		,				[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]		,				)