



"""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        __snake_case       (	_lowercase  ,     unittest.TestCase):
  snake_case__				:   int  			=						GPTaTokenizer
  snake_case__				:   Dict  			=						GPTaTokenizerFast
  snake_case__				:   Union[str, Any]  			=						True
  snake_case__				:   Dict  			=						{"add_prefix_space": True}
  snake_case__				:   List[Any]  			=						False




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         super().setUp()

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

         _lowerCamelCase						:				Any    			= os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']		)
         _lowerCamelCase						:				List[Any]    			= 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(__lowerCAmelCase		) + '''\n'''		)
         with open(self.merges_file						,					'''w'''						,					encoding='''utf-8'''		) as fp:
                fp.write('''\n'''.join(__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Any						,					**__lowerCAmelCase     :						str		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					**__lowerCAmelCase     :						Union[str, Any]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE				( self     :						int						,					__lowerCAmelCase     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Union[str, Any]    			= '''lower newer'''
         _lowerCamelCase						:				List[str]    			= '''lower newer'''
         return input_text, output_text




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[int]    			= GPTaTokenizer(self.vocab_file						,					self.merges_file						,					**self.special_tokens_map		)
         _lowerCamelCase						:				Any    			= '''lower newer'''
         _lowerCamelCase						:				Tuple    			= ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         _lowerCamelCase						:				Any    			= tokenizer.tokenize(__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						,					__lowerCAmelCase		)

         _lowerCamelCase						:				Any    			= tokens + [tokenizer.unk_token]
         _lowerCamelCase						:				Any    			= [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,					__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






         if not self.test_rust_tokenizer:
                return

         _lowerCamelCase						:				List[str]    			= self.get_tokenizer()
         _lowerCamelCase						:				Optional[int]    			= self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase		)

         _lowerCamelCase						:				List[str]    			= '''lower newer'''

         # Testing tokenization
         _lowerCamelCase						:				Dict    			= tokenizer.tokenize(__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[Any]    			= rust_tokenizer.tokenize(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						,					__lowerCAmelCase		)

         # Testing conversion to ids without special tokens
         _lowerCamelCase						:				Optional[Any]    			= tokenizer.encode(__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= rust_tokenizer.encode(__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						,					__lowerCAmelCase		)

         # Testing conversion to ids with special tokens
         _lowerCamelCase						:				List[Any]    			= self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= tokenizer.encode(__lowerCAmelCase						,					add_prefix_space=__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= rust_tokenizer.encode(__lowerCAmelCase		)
         self.assertListEqual(__lowerCAmelCase						,					__lowerCAmelCase		)

         # Testing the unknown token
         _lowerCamelCase						:				Union[str, Any]    			= tokens + [rust_tokenizer.unk_token]
         _lowerCamelCase						:				List[str]    			= [1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,					__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						str						,					*__lowerCAmelCase     :						Tuple						,					**__lowerCAmelCase     :						List[str]		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						int=1_5		):






         """simple docstring"""






         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                       _lowerCamelCase						:				List[Any]    			= self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase						,					**__lowerCAmelCase		)

                       # Simple input
                       _lowerCamelCase						:				Union[str, Any]    			= '''This is a simple input'''
                       _lowerCamelCase						:				int    			= ['''This is a simple input 1''', '''This is a simple input 2''']
                       _lowerCamelCase						:				Any    			= ('''This is a simple input''', '''This is a pair''')
                       _lowerCamelCase						:				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(__lowerCAmelCase						,					tokenizer_r.encode						,					__lowerCAmelCase						,					max_length=__lowerCAmelCase						,					padding='''max_length'''		)

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

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

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

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

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




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[Any]		):






         """simple docstring"""






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

         # Simple input
         _lowerCamelCase						:				Optional[int]    			= '''This is a simple input'''
         _lowerCamelCase						:				Dict    			= ['''This is a simple input looooooooong''', '''This is a simple input''']
         _lowerCamelCase						:				Any    			= ('''This is a simple input''', '''This is a pair''')
         _lowerCamelCase						:				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'''),
         ]

         _lowerCamelCase						:				Tuple    			= tokenizer.pad_token_id

         _lowerCamelCase						:				Optional[int]    			= tokenizer(__lowerCAmelCase						,					padding='''max_length'''						,					max_length=3_0						,					return_tensors='''np'''		)
         _lowerCamelCase						:				Optional[int]    			= tokenizer(__lowerCAmelCase						,					padding=__lowerCAmelCase						,					truncate=__lowerCAmelCase						,					return_tensors='''np'''		)
         _lowerCamelCase						:				List[str]    			= tokenizer(*__lowerCAmelCase						,					padding='''max_length'''						,					max_length=6_0						,					return_tensors='''np'''		)
         _lowerCamelCase						:				Any    			= tokenizer(__lowerCAmelCase						,					padding=__lowerCAmelCase						,					truncate=__lowerCAmelCase						,					return_tensors='''np'''		)

         # s
         # test single string max_length padding
         self.assertEqual(out_s['''input_ids'''].shape[-1]						,					3_0		)
         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]						,					3_3		)
         # 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]						,					6_0		)
         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]						,					5_2		)
         # 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 						SCREAMING_SNAKE_CASE				( self     :						Union[str, Any]		):






         """simple docstring"""






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

         _lowerCamelCase						:				int    			= '''This is a simple input'''
         _lowerCamelCase						:				Optional[Any]    			= ['''This is a simple input 1''', '''This is a simple input 2''']

         _lowerCamelCase						:				Optional[int]    			= tokenizer.bos_token_id

         _lowerCamelCase						:				Union[str, Any]    			= tokenizer(__lowerCAmelCase		)
         _lowerCamelCase						:				Union[str, Any]    			= tokenizer(__lowerCAmelCase		)

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

         _lowerCamelCase						:				str    			= tokenizer.decode(out_s.input_ids		)
         _lowerCamelCase						:				Optional[Any]    			= tokenizer.batch_decode(out_sa.input_ids		)

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




  def 						SCREAMING_SNAKE_CASE				( self     :						str		):






         """simple docstring"""






         pass




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= [self.get_tokenizer(do_lower_case=__lowerCAmelCase						,					add_bos_token=__lowerCAmelCase		)]
         for tokenizer in tokenizers:
                with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                       _lowerCamelCase						:				Any    			= '''Encode this.'''
                       _lowerCamelCase						:				List[Any]    			= '''This one too please.'''
                       _lowerCamelCase						:				Optional[Any]    			= tokenizer.encode(__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase		)
                       encoded_sequence += tokenizer.encode(__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase		)
                       _lowerCamelCase						:				str    			= tokenizer.encode_plus(
                           __lowerCAmelCase						,					__lowerCAmelCase						,					add_special_tokens=__lowerCAmelCase						,					return_special_tokens_mask=__lowerCAmelCase						,					)
                       _lowerCamelCase						:				str    			= encoded_sequence_dict['''input_ids''']
                       _lowerCamelCase						:				List[str]    			= encoded_sequence_dict['''special_tokens_mask''']
                       self.assertEqual(len(__lowerCAmelCase		)						,					len(__lowerCAmelCase		)		)

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






@require_tokenizers
class        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Dict    			= AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,					from_slow=__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= '''A photo of a cat'''

         _lowerCamelCase						:				str    			= tokenizer.encode(
             __lowerCAmelCase						,					)
         self.assertEqual(__lowerCAmelCase						,					[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)
         tokenizer.save_pretrained('''test_opt'''		)

         _lowerCamelCase						:				List[Any]    			= AutoTokenizer.from_pretrained('''./test_opt'''		)
         _lowerCamelCase						:				List[str]    			= tokenizer.encode(
             __lowerCAmelCase						,					)
         self.assertEqual(__lowerCAmelCase						,					[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				int    			= AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,					use_slow=__lowerCAmelCase		)
         _lowerCamelCase						:				int    			= '''A photo of a cat'''

         _lowerCamelCase						:				Any    			= tokenizer.encode(
             __lowerCAmelCase						,					)
         # Same as above
         self.assertEqual(__lowerCAmelCase						,					[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)




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






         """simple docstring"""






         _lowerCamelCase						:				List[Any]    			= AutoTokenizer.from_pretrained('''facebook/opt-350m'''						,					from_slow=__lowerCAmelCase		)

         _lowerCamelCase						:				Dict    			= '''bos'''
         _lowerCamelCase						:				int    			= tokenizer.get_vocab()['''bos''']

         _lowerCamelCase						:				List[Any]    			= '''A photo of a cat'''
         _lowerCamelCase						:				List[str]    			= tokenizer.encode(
             __lowerCAmelCase						,					)
         # We changed the bos token
         self.assertEqual(__lowerCAmelCase						,					[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)
         tokenizer.save_pretrained('''./tok'''		)
         _lowerCamelCase						:				Any    			= AutoTokenizer.from_pretrained('''./tok'''		)
         self.assertTrue(tokenizer.is_fast		)
         _lowerCamelCase						:				int    			= tokenizer.encode(
             __lowerCAmelCase						,					)
         self.assertEqual(__lowerCAmelCase						,					[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]		)



"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__lowerCAmelCase    :       Union[str, Any]					     =0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__lowerCAmelCase    :       Optional[int]					     =[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	WATERMARK_BITS
            lowercase														=	WatermarkEncoder()

            self.encoder.set_watermark("""bits"""		,       self.watermark						)







      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            if images.shape[-1] < 256:
                  return images

            lowercase														=	(255 * (images / 2 + 0.5)).cpu().permute(0		,       2		,       3		,       1						).float().numpy()

            lowercase														=	[self.encoder.encode(__lowerCAmelCase		,       """dwtDct"""						) for image in images]

            lowercase														=	torch.from_numpy(np.array(__lowerCAmelCase						)						).permute(0		,       3		,       1		,       2						)

            lowercase														=	torch.clamp(2 * (images / 255 - 0.5)		,       min=-1.0		,       max=1.0						)
            return images



