



"""simple docstring"""

from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


A_	:						Tuple				=			logging.get_logger(__name__)

A_	:						Optional[Any]				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A_	:						List[Any]				=			{
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

A_	:						str				=			{
    'openai/clip-vit-base-patch32': 77,
}
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		List[str]     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Dict     	=   ['''input_ids''', '''attention_mask''']
	_SCREAMING_SNAKE_CASE  :		int     	=   CLIPTokenizer





	def __init__(			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					List[str]=None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]="<|endoftext|>"    ,					_SCREAMING_SNAKE_CASE							:					List[str]="<|startoftext|>"    ,					_SCREAMING_SNAKE_CASE							:					Tuple="<|endoftext|>"    ,					_SCREAMING_SNAKE_CASE							:					List[str]="<|endoftext|>"    ,					**_SCREAMING_SNAKE_CASE							:					Dict    ,					)	->  Dict:





					"""simple docstring"""


					super().__init__(
					    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

					if not isinstance(self.backend_tokenizer.pre_tokenizer    ,					pre_tokenizers.Sequence						):
									raise ValueError(
									    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
									    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
									    ' to be compatible with this version.The easiest way to do so is'
									    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
									    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
									    ' transformers.'						)

					self._wrap_decode_method_backend_tokenizer()





	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    self.backend_tokenizer.decode

					def new_decode_method(*_SCREAMING_SNAKE_CASE							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Union[str, Any]						):
									SCREAMING_SNAKE_CASE	:       Tuple 							=    orig_decode_method(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    text.replace(self.backend_tokenizer.model.end_of_word_suffix    ,					' '						).strip()
									return text

					SCREAMING_SNAKE_CASE	:       str 							=    new_decode_method





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					List[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None						)	->  List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    [self.bos_token_id]
					SCREAMING_SNAKE_CASE	:       Any 							=    [self.eos_token_id]

					if token_ids_a is None:
									return bos_token + token_ids_a + eos_token
					return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					List[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None						)	->  List[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    [self.bos_token_id]
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    [self.eos_token_id]

					if token_ids_a is None:
									return len(bos_token + token_ids_a + eos_token						) * [0]
					return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token						) * [0]







	def  _lowerCAmelCase    (			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					Optional[str] = None						)	->  Tuple[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self._tokenizer.model.save(_SCREAMING_SNAKE_CASE    ,					name=_SCREAMING_SNAKE_CASE						)
					return tuple(_SCREAMING_SNAKE_CASE						)










"""simple docstring"""

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





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





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





