





'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


class 					SCREAMING_SNAKE_CASE       (_snake_case   ,						unittest.TestCase				):
     lowerCAmelCase						     =			CanineTokenizer
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().setUp()
          __A		:					List[str]						=			CanineTokenizer()
          tokenizer.save_pretrained(self.tmpdirname)


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return CanineTokenizer.from_pretrained('google/canine-s')


     def 				SCREAMING_SNAKE_CASE    (		self		,      **_UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			self.tokenizer_class.from_pretrained(self.tmpdirname		,      **_lowerCamelCase)
          __A		:					List[Any]						=			1024
          return tokenizer


     @require_torch
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.canine_tokenizer
          __A		:					Tuple						=			['''Life is like a box of chocolates.''', '''You never know what you\'re gonna get.''']
          # fmt: off
          __A		:					Any						=			[5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
          # fmt: on
          __A		:					Dict						=			tokenizer(_lowerCamelCase		,      padding=_lowerCamelCase		,      return_tensors='pt')
          self.assertIsInstance(_lowerCamelCase		,      _lowerCamelCase)

          __A		:					str						=			list(batch.input_ids.numpy()[0])

          self.assertListEqual(_lowerCamelCase		,      _lowerCamelCase)

          self.assertEqual((2, 39)		,      batch.input_ids.shape)
          self.assertEqual((2, 39)		,      batch.attention_mask.shape)


     @require_torch
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			self.canine_tokenizer
          __A		:					Optional[int]						=			['''Once there was a man.''', '''He wrote a test in HuggingFace Tranformers.''']
          __A		:					Dict						=			tokenizer(_lowerCamelCase		,      padding=_lowerCamelCase		,      return_tensors='pt')
          # check if input_ids, attention_mask and token_type_ids are returned
          self.assertIn('input_ids'		,      _lowerCamelCase)
          self.assertIn('attention_mask'		,      _lowerCamelCase)
          self.assertIn('token_type_ids'		,      _lowerCamelCase)


     @require_torch
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			self.canine_tokenizer
          __A		:					str						=			[
              '''What\'s the weater?''',
              '''It\'s about 25 degrees.''',
          ]
          __A		:					List[str]						=			tokenizer(
              text_target=_lowerCamelCase		,      max_length=32		,      padding='max_length'		,      truncation=_lowerCamelCase		,      return_tensors='pt')
          self.assertEqual(32		,      targets['input_ids'].shape[1])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			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
          __A		:					Tuple						=			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		:					List[str]						=			tempfile.mkdtemp()

                    __A		:					Any						=			''' He is very happy, UNwant\u00E9d,running'''
                    __A		:					Dict						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    tokenizer.save_pretrained(_lowerCamelCase)

                    __A		:					Optional[Any]						=			tokenizer.__class__.from_pretrained(_lowerCamelCase)
                    __A		:					str						=			after_tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    self.assertListEqual(_lowerCamelCase		,      _lowerCamelCase)

                    shutil.rmtree(_lowerCamelCase)

          __A		:					str						=			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
                    __A		:					Any						=			tempfile.mkdtemp()

                    __A		:					Optional[Any]						=			''' He is very happy, UNwant\u00E9d,running'''

                    __A		:					Optional[int]						=			tokenizer.additional_special_tokens

                    # We can add a new special token for Canine as follows:
                    __A		:					str						=			chr(0Xe_0_0_7)
                    additional_special_tokens.append(_lowerCamelCase)
                    tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
                    __A		:					Tuple						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    tokenizer.save_pretrained(_lowerCamelCase)

                    __A		:					int						=			tokenizer.__class__.from_pretrained(_lowerCamelCase)
                    __A		:					Dict						=			after_tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    self.assertListEqual(_lowerCamelCase		,      _lowerCamelCase)
                    self.assertIn(_lowerCamelCase		,      after_tokenizer.additional_special_tokens)
                    self.assertEqual(after_tokenizer.model_max_length		,      42)

                    __A		:					str						=			tokenizer.__class__.from_pretrained(_lowerCamelCase		,      model_max_length=43)
                    self.assertEqual(tokenizer.model_max_length		,      43)

                    shutil.rmtree(_lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			self.get_tokenizers(do_lower_case=_lowerCamelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    __A		:					Tuple						=			self.get_clean_sequence(_lowerCamelCase)

                    # a special token for Canine can be defined as follows:
                    __A		:					Tuple						=			0Xe_0_0_5
                    __A		:					Optional[int]						=			chr(_lowerCamelCase)

                    tokenizer.add_special_tokens({'cls_token': special_token})
                    __A		:					Optional[int]						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    self.assertEqual(len(_lowerCamelCase)		,      1)

                    __A		:					List[Any]						=			tokenizer.decode(ids + encoded_special_token		,      clean_up_tokenization_spaces=_lowerCamelCase)
                    __A		:					List[str]						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)

                    __A		:					Tuple						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    __A		:					Optional[Any]						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    self.assertEqual(_lowerCamelCase		,      input_encoded + special_token_id)

                    __A		:					Any						=			tokenizer.decode(_lowerCamelCase		,      skip_special_tokens=_lowerCamelCase)
                    self.assertTrue(special_token not in decoded)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			self.get_tokenizers(do_lower_case=_lowerCamelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    __A		:					int						=			chr(0Xe_0_0_5)
                    __A		:					str						=			chr(0Xe_0_0_6)

                    # `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
                    tokenizer.add_tokens([SPECIAL_TOKEN_1]		,      special_tokens=_lowerCamelCase)
                    # `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
                    # which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
                    tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]})

                    __A		:					Optional[int]						=			tokenizer.tokenize(_lowerCamelCase)
                    __A		:					Optional[Any]						=			tokenizer.tokenize(_lowerCamelCase)

                    self.assertEqual(len(_lowerCamelCase)		,      1)
                    self.assertEqual(len(_lowerCamelCase)		,      1)
                    self.assertEqual(token_a[0]		,      _lowerCamelCase)
                    self.assertEqual(token_a[0]		,      _lowerCamelCase)


     @require_tokenizers
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.get_tokenizers(do_lower_case=_lowerCamelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    # a special token for Canine can be defined as follows:
                    __A		:					Optional[int]						=			0Xe_0_0_6
                    __A		:					List[str]						=			chr(_lowerCamelCase)

                    __A		:					Optional[int]						=			AddedToken(_lowerCamelCase		,      lstrip=_lowerCamelCase)
                    tokenizer.add_special_tokens({'additional_special_tokens': [new_token]})

                    with tempfile.TemporaryDirectory() as tmp_dir_name:
                         tokenizer.save_pretrained(_lowerCamelCase)
                         tokenizer.from_pretrained(_lowerCamelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					int						=			[]
          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		:					Optional[Any]						=			json.load(_lowerCamelCase)

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

                    # a special token for Canine can be defined as follows:
                    __A		:					int						=			0Xe_0_0_6
                    __A		:					Optional[Any]						=			chr(_lowerCamelCase)

                    __A		:					Union[str, Any]						=			[new_token_a]
                    __A		:					Optional[int]						=			[new_token_a]

                    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		:					str						=			tokenizer_class.from_pretrained(_lowerCamelCase		,      extra_ids=0)
                    self.assertIn(_lowerCamelCase		,      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(
                        [new_token_a]		,      tokenizer_without_change_in_init.convert_ids_to_tokens(
                            tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]))		,      )

                    __A		:					Optional[int]						=			0Xe_0_0_7
                    __A		:					Any						=			chr(_lowerCamelCase)
                    # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                    __A		:					Tuple						=			[AddedToken(_lowerCamelCase		,      lstrip=_lowerCamelCase)]
                    __A		:					Optional[Any]						=			tokenizer_class.from_pretrained(
                        _lowerCamelCase		,      additional_special_tokens=_lowerCamelCase		,      extra_ids=0)

                    self.assertIn(_lowerCamelCase		,      tokenizer.additional_special_tokens)
                    # self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
                    self.assertEqual(
                        [new_token_a]		,      tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a])))


     @require_tokenizers
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[Any]						=			self.get_tokenizers(do_lower_case=_lowerCamelCase)
          for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                    __A		:					List[str]						=			'''hello world'''
                    if self.space_between_special_tokens:
                         __A		:					Optional[Any]						=			'''[CLS] hello world [SEP]'''
                    else:
                         __A		:					Tuple						=			input
                    __A		:					Any						=			tokenizer.encode(_lowerCamelCase		,      add_special_tokens=_lowerCamelCase)
                    __A		:					List[Any]						=			tokenizer.decode(_lowerCamelCase		,      spaces_between_special_tokens=self.space_between_special_tokens)
                    self.assertIn(_lowerCamelCase		,      [output, output.lower()])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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

          __A		:					str						=			'''a'''
          __A		:					List[Any]						=			ord(_lowerCamelCase)

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

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

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

          __A		:					List[Any]						=			0Xe_0_0_6
          __A		:					str						=			chr(_lowerCamelCase)
          setattr(_lowerCamelCase		,      'additional_special_tokens_ids'		,      [additional_special_token_id])
          self.assertListEqual(getattr(_lowerCamelCase		,      'additional_special_tokens')		,      [additional_special_token])
          self.assertListEqual(getattr(_lowerCamelCase		,      'additional_special_tokens_ids')		,      [additional_special_token_id])


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass



import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			UNetaDModel(
           block_out_channels=(32, 64)   ,    layers_per_block=2   ,    sample_size=32   ,    in_channels=3   ,    out_channels=3   ,    down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')   ,    up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')   ,    )
       return model




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2







@slow
@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

       assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2





