



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class 	lowerCamelCase__	(       _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Optional[Any] =  VQModel
 a__					: Optional[int] =  "sample"

 @property
 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Any=(32, 32)      )   ->	int:
    _A      =       4
    _A      =       3

    _A      =       floats_tensor((batch_size, num_channels) + sizes      ).to(__lowerCAmelCase      )

    return {"sample": image}

 @property
 def     snake_case_      (    self					:					Optional[Any]      )   ->	List[Any]:
    return (3, 32, 32)

 @property
 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    return (3, 32, 32)

 def     snake_case_      (    self					:					Any      )   ->	Tuple:
    _A      =       {
        '''block_out_channels''': [32, 64],
        '''in_channels''': 3,
        '''out_channels''': 3,
        '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
        '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
        '''latent_channels''': 3,
    }
    _A      =       self.dummy_input
    return init_dict, inputs_dict

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

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

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Any:
    _A  ,    _A      =       VQModel.from_pretrained('''fusing/vqgan-dummy'''			,						output_loading_info=__lowerCAmelCase      )
    self.assertIsNotNone(__lowerCAmelCase      )
    self.assertEqual(len(loading_info['''missing_keys''']      )			,						0      )

    model.to(__lowerCAmelCase      )
    _A      =       model(**self.dummy_input      )

    assert image is not None, "Make sure output is not None"

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[Any]:
    _A      =       VQModel.from_pretrained('''fusing/vqgan-dummy'''      )
    model.to(__lowerCAmelCase      ).eval()

    torch.manual_seed(0      )
    if torch.cuda.is_available():
       torch.cuda.manual_seed_all(0      )

    _A      =       torch.randn(1			,						model.config.in_channels			,						model.config.sample_size			,						model.config.sample_size      )
    _A      =       image.to(__lowerCAmelCase      )
    with torch.no_grad():
       _A      =       model(__lowerCAmelCase      ).sample

    _A      =       output[0, -1, -3:, -3:].flatten().cpu()
    # fmt: off
    _A      =       torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
    # fmt: on
    self.assertTrue(torch.allclose(__lowerCAmelCase			,						__lowerCAmelCase			,						atol=1E-3      )      )






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

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

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
      __lowercase				      =						"""pt"""
elif is_tf_available():
      __lowercase				      =						"""tf"""
else:
      __lowercase				      =						"""jax"""

class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Dict  					=    PerceiverTokenizer
   _lowercase     :       Dict  					=    False

   def     UpperCamelCase					(				self	:    Tuple       ) ->			Any:
         """simple docstring"""





         super().setUp()
         A_							       =		PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname       )

   @cached_property
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''       )

   def     UpperCamelCase					(				self	:    List[str]			,						**lowerCamelCase__	:    str       ) ->			PerceiverTokenizer:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    List[str]=2_0			,						lowerCamelCase__	:    Union[str, Any]=5       ) ->			Tuple[str, list]:
         """simple docstring"""





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

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

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

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		'''Unicode €.'''
         A_							       =		tokenizer(lowerCamelCase__       )
         A_							       =		[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )

         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]Unicode €.[SEP]'''       )

         A_							       =		tokenizer('''e è é ê ë'''       )
         A_							       =		[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['''input_ids''']			,						lowerCamelCase__       )
         # decoding
         A_							       =		tokenizer.decode(lowerCamelCase__       )
         self.assertEqual(lowerCamelCase__			,						'''[CLS]e è é ê ë[SEP]'''       )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''       )       )			,						'''[CLS]e è é ê ë[SEP]'''       )

   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         A_							       =		[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         A_							       =		tokenizer(lowerCamelCase__			,						padding=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )

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

         self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

         self.assertEqual((2, 3_8)			,						batch.input_ids.shape       )
         self.assertEqual((2, 3_8)			,						batch.attention_mask.shape       )

   def     UpperCamelCase					(				self	:    str       ) ->			List[str]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Any       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         A_							       =		[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         A_							       =		tokenizer(
             text_target=lowerCamelCase__			,						max_length=3_2			,						padding='''max_length'''			,						truncation=lowerCamelCase__			,						return_tensors=lowerCamelCase__       )
         self.assertEqual(3_2			,						targets['''input_ids'''].shape[1]       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.get_tokenizers()
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     self.assertNotEqual(tokenizer.model_max_length			,						4_2       )

        # Now let's start the test
         A_							       =		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
                     A_							       =		tempfile.mkdtemp()

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

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )

                     shutil.rmtree(lowerCamelCase__       )

         A_							       =		self.get_tokenizers(model_max_length=4_2       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     # Isolate this from the other tests because we save additional tokens/etc
                     A_							       =		tempfile.mkdtemp()

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

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__       )
                     A_							       =		after_tokenizer.encode(lowerCamelCase__			,						add_special_tokens=lowerCamelCase__       )
                     self.assertListEqual(lowerCamelCase__			,						lowerCamelCase__       )
                     self.assertIn('''new_additional_special_token'''			,						after_tokenizer.additional_special_tokens       )
                     self.assertEqual(after_tokenizer.model_max_length			,						4_2       )

                     A_							       =		tokenizer.__class__.from_pretrained(lowerCamelCase__			,						model_max_length=4_3       )
                     self.assertEqual(tokenizer.model_max_length			,						4_3       )

                     shutil.rmtree(lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		[]
         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(lowerCamelCase__       )

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

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

                     A_							       =		[F"<extra_id_{i}>" for i in range(1_2_5       )]

                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]
                     A_							       =		added_tokens_extra_ids + [
                         '''an_additional_special_token'''
                     ]

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

                     # 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
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						)
                     self.assertIn(
                         '''an_additional_special_token'''			,						tokenizer_without_change_in_init.additional_special_tokens       )
                     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
                     A_							       =		added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''			,						lstrip=lowerCamelCase__       )]
                     A_							       =		tokenizer_class.from_pretrained(
                         lowerCamelCase__			,						additional_special_tokens=lowerCamelCase__			,						)

                     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     UpperCamelCase					(				self	:    Dict       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]       )			,						'''�'''       )

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			int:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    List[str]       ) ->			Dict:
         """simple docstring"""





         pass

   def     UpperCamelCase					(				self	:    int       ) ->			Any:
         """simple docstring"""





         pass





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.get_tokenizers(fast=lowerCamelCase__			,						do_lower_case=lowerCamelCase__       )
         for tokenizer in tokenizers:
               with self.subTest(F"{tokenizer.__class__.__name__}"       ):
                     A_							       =		['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                     A_							       =		tokenizer.convert_tokens_to_string(lowerCamelCase__       )

                     self.assertIsInstance(lowerCamelCase__			,						lowerCamelCase__       )





