

'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     lowerCamelCase :      Tuple					 =     "pt"
elif is_tf_available():
     lowerCamelCase :      Optional[int]					 =     "tf"
else:
     lowerCamelCase :      Tuple					 =     "jax"







class 							A__    (       A__     ,   unittest.TestCase  ):
  A__		 =				ByTaTokenizer
  A__		 =				False





  def 						A  (		self      :	str )	->    Any:





       '''simple docstring'''






       super().setUp()
       _SCREAMING_SNAKE_CASE							    =ByTaTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )





  @cached_property
  def 						A  (		self      :	List[Any] )	->    Union[str, Any]:





       '''simple docstring'''






       return ByTaTokenizer.from_pretrained('google/byt5-small' )





  def 						A  (		self      :	Dict							,					**_a      :	Union[str, Any] )	->    ByTaTokenizer:





       '''simple docstring'''






       return self.tokenizer_class.from_pretrained(self.tmpdirname							,					**_a )





  def 						A  (		self      :	List[str]							,					_a      :	Dict							,					_a      :	str=False							,					_a      :	Optional[int]=20							,					_a      :	int=5 )	->    Tuple[str, list]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       for i in range(len(_a ) ):
            try:
                 _SCREAMING_SNAKE_CASE							    =tokenizer.decode([i]							,					clean_up_tokenization_spaces=_a )
            except UnicodeDecodeError:
                 pass
            toks.append((i, tok) )

       _SCREAMING_SNAKE_CASE							    =list(filter(lambda _a : re.match(r'^[ a-zA-Z]+$'							,					t[1] )							,					_a ) )
       _SCREAMING_SNAKE_CASE							    =list(filter(lambda _a : [t[0]] == tokenizer.encode(t[1]							,					add_special_tokens=_a )							,					_a ) )
       if max_length is not None and len(_a ) > max_length:
            _SCREAMING_SNAKE_CASE							    =toks[:max_length]
       if min_length is not None and len(_a ) < min_length and len(_a ) > 0:
            while len(_a ) < min_length:
                 _SCREAMING_SNAKE_CASE							    =toks + toks
        # toks_str = [t[1] for t in toks]
       _SCREAMING_SNAKE_CASE							    =[t[0] for t in toks]

       # Ensure consistency
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(_a							,					clean_up_tokenization_spaces=_a )
       if " " not in output_txt and len(_a ) > 1:
            _SCREAMING_SNAKE_CASE							    =(
                tokenizer.decode([toks_ids[0]]							,					clean_up_tokenization_spaces=_a )
                + ' '
                + tokenizer.decode(toks_ids[1:]							,					clean_up_tokenization_spaces=_a )
            )
       if with_prefix_space:
            _SCREAMING_SNAKE_CASE							    =' ' + output_txt
       _SCREAMING_SNAKE_CASE							    =tokenizer.encode(_a							,					add_special_tokens=_a )
       return output_txt, output_ids





  def 						A  (		self      :	Any )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    =tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'] )
       _SCREAMING_SNAKE_CASE							    =tokenizer(['hi', 'I went to the gym', ''] )
       self.assertListEqual(batch_with_eos_added['input_ids']							,					batch_without_eos_added['input_ids'] )





  def 						A  (		self      :	Optional[int] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    ='Unicode €.'
       _SCREAMING_SNAKE_CASE							    =tokenizer(_a )
       _SCREAMING_SNAKE_CASE							    =[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
       self.assertEqual(encoded['input_ids']							,					_a )

       # decoding
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(_a )
       self.assertEqual(_a							,					'Unicode €.</s>' )

       _SCREAMING_SNAKE_CASE							    =tokenizer('e è é ê ë' )
       _SCREAMING_SNAKE_CASE							    =[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
       self.assertEqual(encoded['input_ids']							,					_a )
       # decoding
       _SCREAMING_SNAKE_CASE							    =tokenizer.decode(_a )
       self.assertEqual(_a							,					'e è é ê ë</s>' )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë' ) )							,					'e è é ê ë</s>' )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    =['A long paragraph for summarization.', 'Another paragraph for summarization.']
       # fmt: off
       _SCREAMING_SNAKE_CASE							    =[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
       # fmt: on
       _SCREAMING_SNAKE_CASE							    =tokenizer(_a							,					padding=_a							,					return_tensors=_a )
       self.assertIsInstance(_a							,					_a )

       if FRAMEWORK != "jax":
            _SCREAMING_SNAKE_CASE							    =list(batch.input_ids.numpy()[0] )
       else:
            _SCREAMING_SNAKE_CASE							    =list(batch.input_ids.tolist()[0] )

       self.assertListEqual(_a							,					_a )

       self.assertEqual((2, 37)							,					batch.input_ids.shape )
       self.assertEqual((2, 37)							,					batch.attention_mask.shape )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    =['A long paragraph for summarization.', 'Another paragraph for summarization.']
       _SCREAMING_SNAKE_CASE							    =tokenizer(_a							,					padding=_a							,					return_tensors=_a )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn('input_ids'							,					_a )
       self.assertIn('attention_mask'							,					_a )
       self.assertNotIn('decoder_input_ids'							,					_a )
       self.assertNotIn('decoder_attention_mask'							,					_a )





  def 						A  (		self      :	List[str] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    =[
           'Summary of the text.',
           'Another summary.',
       ]
       _SCREAMING_SNAKE_CASE							    =tokenizer(
           text_target=_a							,					max_length=32							,					padding='max_length'							,					truncation=_a							,					return_tensors=_a )
       self.assertEqual(32							,					targets['input_ids'].shape[1] )





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.ta_base_tokenizer
       _SCREAMING_SNAKE_CASE							    =['A long paragraph for summarization. </s>']
       _SCREAMING_SNAKE_CASE							    =['Summary of the text. </s>']
       # fmt: off
       _SCREAMING_SNAKE_CASE							    =[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
       _SCREAMING_SNAKE_CASE							    =[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
       # fmt: on

       _SCREAMING_SNAKE_CASE							    =tokenizer(_a							,					text_target=_a )

       self.assertEqual(_a							,					batch['input_ids'][0] )
       self.assertEqual(_a							,					batch['labels'][0] )





  def 						A  (		self      :	Tuple )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_tokenizers()
       for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}" ):
                 self.assertNotEqual(tokenizer.model_max_length							,					42 )

        # Now let's start the test
       _SCREAMING_SNAKE_CASE							    =self.get_tokenizers()
       for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}" ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 _SCREAMING_SNAKE_CASE							    =tempfile.mkdtemp()

                 _SCREAMING_SNAKE_CASE							    =' He is very happy, UNwant\u00E9d,running'
                 _SCREAMING_SNAKE_CASE							    =tokenizer.encode(_a							,					add_special_tokens=_a )
                 tokenizer.save_pretrained(_a )

                 _SCREAMING_SNAKE_CASE							    =tokenizer.__class__.from_pretrained(_a )
                 _SCREAMING_SNAKE_CASE							    =after_tokenizer.encode(_a							,					add_special_tokens=_a )
                 self.assertListEqual(_a							,					_a )

                 shutil.rmtree(_a )

       _SCREAMING_SNAKE_CASE							    =self.get_tokenizers(model_max_length=42 )
       for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}" ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 _SCREAMING_SNAKE_CASE							    =tempfile.mkdtemp()

                 _SCREAMING_SNAKE_CASE							    =' He is very happy, UNwant\u00E9d,running'
                 tokenizer.add_tokens(['bim', 'bambam'] )
                 _SCREAMING_SNAKE_CASE							    =tokenizer.additional_special_tokens
                 additional_special_tokens.append('new_additional_special_token' )
                 tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
                 _SCREAMING_SNAKE_CASE							    =tokenizer.encode(_a							,					add_special_tokens=_a )
                 tokenizer.save_pretrained(_a )

                 _SCREAMING_SNAKE_CASE							    =tokenizer.__class__.from_pretrained(_a )
                 _SCREAMING_SNAKE_CASE							    =after_tokenizer.encode(_a							,					add_special_tokens=_a )
                 self.assertListEqual(_a							,					_a )
                 self.assertIn('new_additional_special_token'							,					after_tokenizer.additional_special_tokens )
                 self.assertEqual(after_tokenizer.model_max_length							,					42 )

                 _SCREAMING_SNAKE_CASE							    =tokenizer.__class__.from_pretrained(_a							,					model_max_length=43 )
                 self.assertEqual(tokenizer.model_max_length							,					43 )

                 shutil.rmtree(_a )





  def 						A  (		self      :	int )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

       if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(_a )

                 with open(os.path.join(_a							,					'special_tokens_map.json' )							,					encoding='utf-8' ) as json_file:
                      _SCREAMING_SNAKE_CASE							    =json.load(_a )

                 with open(os.path.join(_a							,					'tokenizer_config.json' )							,					encoding='utf-8' ) as json_file:
                      _SCREAMING_SNAKE_CASE							    =json.load(_a )

                 _SCREAMING_SNAKE_CASE							    =[f"<extra_id_{i}>" for i in range(125 )]

                 _SCREAMING_SNAKE_CASE							    =added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]
                 _SCREAMING_SNAKE_CASE							    =added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]

                 with open(os.path.join(_a							,					'special_tokens_map.json' )							,					'w'							,					encoding='utf-8' ) as outfile:
                      json.dump(_a							,					_a )
                 with open(os.path.join(_a							,					'tokenizer_config.json' )							,					'w'							,					encoding='utf-8' ) as outfile:
                      json.dump(_a							,					_a )

                 # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                 # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                 # "special_tokens_map.json" files
                 _SCREAMING_SNAKE_CASE							    =tokenizer_class.from_pretrained(
                     _a							,					)
                 self.assertIn(
                     'an_additional_special_token'							,					tokenizer_without_change_in_init.additional_special_tokens )
                 # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
                 self.assertEqual(
                     ['an_additional_special_token']							,					tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'] ) )							,					)

                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 _SCREAMING_SNAKE_CASE							    =added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'							,					lstrip=_a )]
                 _SCREAMING_SNAKE_CASE							    =tokenizer_class.from_pretrained(
                     _a							,					additional_special_tokens=_a							,					)

                 self.assertIn('a_new_additional_special_token'							,					tokenizer.additional_special_tokens )
                 self.assertEqual(
                     ['a_new_additional_special_token']							,					tokenizer.convert_ids_to_tokens(
                         tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'] ) )							,					)





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





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =[]
       if self.test_slow_tokenizer:
            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )

       if self.test_rust_tokenizer:
            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
            with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(_a )

                 _SCREAMING_SNAKE_CASE							    =tokenizer_class.from_pretrained(_a )

                 self.assertTrue(tokenizer.decode([255] ) == '' )





  def 						A  (		self      :	int )	->    List[Any]:





       '''simple docstring'''






       pass





  def 						A  (		self      :	Optional[int] )	->    Optional[Any]:





       '''simple docstring'''






       pass





  def 						A  (		self      :	List[Any] )	->    Tuple:





       '''simple docstring'''






       pass





  def 						A  (		self      :	Tuple )	->    Optional[Any]:





       '''simple docstring'''






       pass





  def 						A  (		self      :	Optional[int] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_tokenizers(fast=_a							,					do_lower_case=_a )
       for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}" ):
                 _SCREAMING_SNAKE_CASE							    =['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
                 _SCREAMING_SNAKE_CASE							    =tokenizer.convert_tokens_to_string(_a )

                 self.assertIsInstance(_a							,					_a )





  def 						A  (		self      :	List[str] )	->    Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =self.get_tokenizers()
       for tokenizer in tokenizers:
            with self.subTest(f"{tokenizer.__class__.__name__}" ):
                 _SCREAMING_SNAKE_CASE							    =[
                     'bos_token',
                     'eos_token',
                     'unk_token',
                     'sep_token',
                     'pad_token',
                     'cls_token',
                     'mask_token',
                 ]

                 _SCREAMING_SNAKE_CASE							    =0
                 _SCREAMING_SNAKE_CASE							    =tokenizer.convert_ids_to_tokens(
                     _a							,					skip_special_tokens=_a )

                 for attr in attributes_list:
                      setattr(_a							,					attr + '_id'							,					_a )
                      self.assertEqual(getattr(_a							,					_a )							,					_a )
                      self.assertEqual(getattr(_a							,					attr + '_id' )							,					_a )

                      setattr(_a							,					attr + '_id'							,					_a )
                      self.assertEqual(getattr(_a							,					_a )							,					_a )
                      self.assertEqual(getattr(_a							,					attr + '_id' )							,					_a )

                 setattr(_a							,					'additional_special_tokens_ids'							,					[] )
                 self.assertListEqual(getattr(_a							,					'additional_special_tokens' )							,					[] )
                 self.assertListEqual(getattr(_a							,					'additional_special_tokens_ids' )							,					[] )

                 setattr(_a							,					'additional_special_tokens_ids'							,					[token_id_to_test_setters] )
                 self.assertListEqual(getattr(_a							,					'additional_special_tokens' )							,					[token_to_test_setters] )
                 self.assertListEqual(getattr(_a							,					'additional_special_tokens_ids' )							,					[token_id_to_test_setters] )



'''simple docstring'''
import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     Any=False				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     if display:
          print(yaml.dump(OmegaConf.to_container(_UpperCamelCase				)				)				)
     return config


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Optional[Any]=None							, _UpperCamelCase						:     Union[str, Any]=None				)      ->			Optional[Any]:
     """simple docstring"""




     if conf_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.yaml'
     _SCREAMING_SNAKE_CASE							    =load_config(_UpperCamelCase							, display=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =VQModel(**config.model.params				)
     if ckpt_path is None:
          _SCREAMING_SNAKE_CASE							    ='./model_checkpoints/vqgan_only.pt'
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location=_UpperCamelCase				)
     if ".ckpt" in ckpt_path:
          _SCREAMING_SNAKE_CASE							    =sd['state_dict']
     model.load_state_dict(_UpperCamelCase							, strict=_UpperCamelCase				)
     model.to(_UpperCamelCase				)
     del sd
     return model


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     int				)      ->			Dict:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =model.encode(_UpperCamelCase				)
     print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"				)
     _SCREAMING_SNAKE_CASE							    =model.decode(_UpperCamelCase				)
     return xrec


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict							, _UpperCamelCase						:     List[str]=False				)      ->			int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =string.rsplit('.'							, 1				)
     if reload:
          _SCREAMING_SNAKE_CASE							    =importlib.import_module(_UpperCamelCase				)
          importlib.reload(_UpperCamelCase				)
     return getattr(importlib.import_module(_UpperCamelCase							, package=_UpperCamelCase				)							, cls				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     str				)      ->			List[str]:
     """simple docstring"""




     if "target" not in config:
          raise KeyError('Expected key `target` to instantiate.'				)
     return get_obj_from_str(config['target']				)(**config.get('params'							, {}				)				)


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     List[Any]							, _UpperCamelCase						:     List[str]=True							, _UpperCamelCase						:     int=True				)      ->			Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =instantiate_from_config(_UpperCamelCase				)
     if sd is not None:
          model.load_state_dict(_UpperCamelCase				)
     if gpu:
          model.cuda()
     if eval_mode:
          model.eval()
     return {"model": model}







def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[Any]							, _UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Optional[int]				)      ->			List[Any]:
     """simple docstring"""




     if ckpt:
          _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)
          _SCREAMING_SNAKE_CASE							    =pl_sd['global_step']
          print(f"loaded model from global step {global_step}."				)
     else:
          _SCREAMING_SNAKE_CASE							    ={'state_dict': None}
          _SCREAMING_SNAKE_CASE							    =None
     _SCREAMING_SNAKE_CASE							    =load_model_from_config(config.model							, pl_sd['state_dict']							, gpu=_UpperCamelCase							, eval_mode=_UpperCamelCase				)['model']
     return model, global_step
