


'''simple docstring'''






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__A	:	str     =     logging.get_logger(__name__)

__A	:	str     =     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A	:	str     =     {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

__A	:	Union[str, Any]     =     {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		str   		=  VOCAB_FILES_NAMES
							lowercase			:		Union[str, Any]   		=  PRETRAINED_VOCAB_FILES_MAP
							lowercase			:		Dict   		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase			:		str   		=  ['input_ids', 'attention_mask']
							lowercase			:		List[Any]   		=  RobertaTokenizer





							def __init__(  self		:int  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Dict="replace"  ,_UpperCamelCase		:List[str]="<s>"  ,_UpperCamelCase		:Dict="</s>"  ,_UpperCamelCase		:List[str]="</s>"  ,_UpperCamelCase		:Any="<s>"  ,_UpperCamelCase		:Tuple="<unk>"  ,_UpperCamelCase		:int="<pad>"  ,_UpperCamelCase		:str="<mask>"  ,_UpperCamelCase		:Dict=False  ,_UpperCamelCase		:Dict=True  ,**_UpperCamelCase		:Any  ,):
									super().__init__(
									    _UpperCamelCase  ,_UpperCamelCase  ,tokenizer_file=_UpperCamelCase  ,errors=_UpperCamelCase  ,bos_token=_UpperCamelCase  ,eos_token=_UpperCamelCase  ,sep_token=_UpperCamelCase  ,cls_token=_UpperCamelCase  ,unk_token=_UpperCamelCase  ,pad_token=_UpperCamelCase  ,mask_token=_UpperCamelCase  ,add_prefix_space=_UpperCamelCase  ,trim_offsets=_UpperCamelCase  ,**_UpperCamelCase  ,)

									snake_case_		:							Optional[int]			       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
									if pre_tok_state.get("""add_prefix_space"""  ,_UpperCamelCase			) != add_prefix_space:
											snake_case_		:							Union[str, Any]			       =						getattr(_UpperCamelCase  ,pre_tok_state.pop("""type"""			)			)
											snake_case_		:							Dict			       =						add_prefix_space
											snake_case_		:							Dict			       =						pre_tok_class(**_UpperCamelCase			)

									snake_case_		:							Any			       =						add_prefix_space

									snake_case_		:							Tuple			       =						"""post_processor"""
									snake_case_		:							str			       =						getattr(self.backend_tokenizer  ,_UpperCamelCase  ,_UpperCamelCase			)
									if tokenizer_component_instance:
											snake_case_		:							str			       =						json.loads(tokenizer_component_instance.__getstate__()			)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
													snake_case_		:							str			       =						tuple(state["""sep"""]			)
											if "cls" in state:
													snake_case_		:							Optional[int]			       =						tuple(state["""cls"""]			)

											snake_case_		:							List[Any]			       =						False

											if state.get("""add_prefix_space"""  ,_UpperCamelCase			) != add_prefix_space:
													snake_case_		:							Tuple			       =						add_prefix_space
													snake_case_		:							Optional[int]			       =						True

											if state.get("""trim_offsets"""  ,_UpperCamelCase			) != trim_offsets:
													snake_case_		:							Union[str, Any]			       =						trim_offsets
													snake_case_		:							Tuple			       =						True

											if changes_to_apply:
													snake_case_		:							List[str]			       =						getattr(_UpperCamelCase  ,state.pop("""type"""			)			)
													snake_case_		:							Any			       =						component_class(**_UpperCamelCase			)
													setattr(self.backend_tokenizer  ,_UpperCamelCase  ,_UpperCamelCase			)





							@property
							def 		a__   (  self		:Union[str, Any]			):
									if self._mask_token is None:
											if self.verbose:
													logger.error("""Using mask_token, but it is not set yet."""			)
											return None
									return str(self._mask_token			)





							@mask_token.setter
							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Any			):
									snake_case_		:							List[Any]			       =						AddedToken(_UpperCamelCase  ,lstrip=_UpperCamelCase  ,rstrip=_UpperCamelCase			) if isinstance(_UpperCamelCase  ,_UpperCamelCase			) else value
									snake_case_		:							Optional[int]			       =						value





							def 		a__   (  self		:str  ,*_UpperCamelCase		:str  ,**_UpperCamelCase		:Tuple			):
									snake_case_		:							str			       =						kwargs.get("""is_split_into_words"""  ,_UpperCamelCase			)
									assert self.add_prefix_space or not is_split_into_words, (
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._batch_encode_plus(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:Dict			):
									snake_case_		:							List[str]			       =						kwargs.get("""is_split_into_words"""  ,_UpperCamelCase			)

									assert self.add_prefix_space or not is_split_into_words, (
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
									    "to use it with pretokenized inputs."
									)

									return super()._encode_plus(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[str] = None			):
									snake_case_		:							Any			       =						self._tokenizer.model.save(_UpperCamelCase  ,name=_UpperCamelCase			)
									return tuple(_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]=None			):
									snake_case_		:							Tuple			       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[int]  ,_UpperCamelCase		:Optional[List[int]] = None			):
									snake_case_		:							List[str]			       =						[self.sep_token_id]
									snake_case_		:							Dict			       =						[self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep			) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]



'''simple docstring'''






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		List[str]   		=  KandinskyVaaPriorPipeline
							lowercase			:		Optional[Any]   		=  ['prompt']
							lowercase			:		Dict   		=  ['prompt', 'negative_prompt']
							lowercase			:		Dict   		=  [
							    'num_images_per_prompt',
							    'generator',
							    'num_inference_steps',
							    'latents',
							    'negative_prompt',
							    'guidance_scale',
							    'output_type',
							    'return_dict',
							]
							lowercase			:		int   		=  False





							@property
							def 		a__   (  self		:int			):
									return 3_2





							@property
							def 		a__   (  self		:Any			):
									return 3_2





							@property
							def 		a__   (  self		:Union[str, Any]			):
									return self.time_input_dim





							@property
							def 		a__   (  self		:int			):
									return self.time_input_dim * 4





							@property
							def 		a__   (  self		:Dict			):
									return 1_0_0





							@property
							def 		a__   (  self		:List[Any]			):
									snake_case_		:							List[Any]			       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
									return tokenizer





							@property
							def 		a__   (  self		:int			):
									torch.manual_seed(0			)
									snake_case_		:							Optional[Any]			       =						CLIPTextConfig(
									    bos_token_id=0  ,eos_token_id=2  ,hidden_size=self.text_embedder_hidden_size  ,projection_dim=self.text_embedder_hidden_size  ,intermediate_size=3_7  ,layer_norm_eps=1E-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_0_0_0  ,)
									return CLIPTextModelWithProjection(_UpperCamelCase			)





							@property
							def 		a__   (  self		:Dict			):
									torch.manual_seed(0			)

									snake_case_		:							Tuple			       =						{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 1_2,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									snake_case_		:							int			       =						PriorTransformer(**_UpperCamelCase			)
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									snake_case_		:							Any			       =						nn.Parameter(torch.ones(model.clip_std.shape			)			)
									return model





							@property
							def 		a__   (  self		:Optional[int]			):
									torch.manual_seed(0			)
									snake_case_		:							Any			       =						CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size  ,image_size=2_2_4  ,projection_dim=self.text_embedder_hidden_size  ,intermediate_size=3_7  ,num_attention_heads=4  ,num_channels=3  ,num_hidden_layers=5  ,patch_size=1_4  ,)

									snake_case_		:							Optional[Any]			       =						CLIPVisionModelWithProjection(_UpperCamelCase			)
									return model





							@property
							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						CLIPImageProcessor(
									    crop_size=2_2_4  ,do_center_crop=_UpperCamelCase  ,do_normalize=_UpperCamelCase  ,do_resize=_UpperCamelCase  ,image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,resample=3  ,size=2_2_4  ,)

									return image_processor





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Tuple			       =						self.dummy_prior
									snake_case_		:							Any			       =						self.dummy_image_encoder
									snake_case_		:							Optional[int]			       =						self.dummy_text_encoder
									snake_case_		:							Any			       =						self.dummy_tokenizer
									snake_case_		:							Union[str, Any]			       =						self.dummy_image_processor

									snake_case_		:							Tuple			       =						UnCLIPScheduler(
									    variance_type="""fixed_small_log"""  ,prediction_type="""sample"""  ,num_train_timesteps=1_0_0_0  ,clip_sample=_UpperCamelCase  ,clip_sample_range=10.0  ,)

									snake_case_		:							Tuple			       =						{
									    """prior""": prior,
									    """image_encoder""": image_encoder,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """scheduler""": scheduler,
									    """image_processor""": image_processor,
									}

									return components





							def 		a__   (  self		:Dict  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Union[str, Any]=0			):
									if str(_UpperCamelCase			).startswith("""mps"""			):
											snake_case_		:							Optional[int]			       =						torch.manual_seed(_UpperCamelCase			)
									else:
											snake_case_		:							int			       =						torch.Generator(device=_UpperCamelCase			).manual_seed(_UpperCamelCase			)
									snake_case_		:							Dict			       =						{
									    """prompt""": """horse""",
									    """generator""": generator,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs





							def 		a__   (  self		:int			):
									snake_case_		:							Union[str, Any]			       =						"""cpu"""

									snake_case_		:							int			       =						self.get_dummy_components()

									snake_case_		:							int			       =						self.pipeline_class(**_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						pipe.to(_UpperCamelCase			)

									pipe.set_progress_bar_config(disable=_UpperCamelCase			)

									snake_case_		:							Any			       =						pipe(**self.get_dummy_inputs(_UpperCamelCase			)			)
									snake_case_		:							List[Any]			       =						output.image_embeds

									snake_case_		:							List[Any]			       =						pipe(
									    **self.get_dummy_inputs(_UpperCamelCase			)  ,return_dict=_UpperCamelCase  ,)[0]

									snake_case_		:							Dict			       =						image[0, -1_0:]
									snake_case_		:							Optional[Any]			       =						image_from_tuple[0, -1_0:]

									assert image.shape == (1, 3_2)

									snake_case_		:							Optional[int]			       =						np.array(
									    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]			)

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





							@skip_mps
							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Any			       =						torch_device == """cpu"""
									snake_case_		:							Any			       =						True
									snake_case_		:							Dict			       =						False

									self._test_inference_batch_single_identical(
									    test_max_difference=_UpperCamelCase  ,relax_max_difference=_UpperCamelCase  ,test_mean_pixel_difference=_UpperCamelCase  ,)





							@skip_mps
							def 		a__   (  self		:str			):
									snake_case_		:							str			       =						torch_device == """cpu"""
									snake_case_		:							List[str]			       =						False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=_UpperCamelCase  ,test_mean_pixel_difference=_UpperCamelCase  ,)