






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    __lowercase   (						lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					KandinskyVaaPriorPipeline
     SCREAMING_SNAKE_CASE     =					["prompt"]
     SCREAMING_SNAKE_CASE     =					["prompt", "negative_prompt"]
     SCREAMING_SNAKE_CASE     =					[
         "num_images_per_prompt",
         "generator",
         "num_inference_steps",
         "latents",
         "negative_prompt",
         "guidance_scale",
         "output_type",
         "return_dict",
     ]
     SCREAMING_SNAKE_CASE     =					False




     @property
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return 32




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         return self.time_input_dim




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         return self.time_input_dim * 4




     @property
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         return 100




     @property
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)
         return tokenizer




     @property
     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         torch.manual_seed(0					)
         __A       =						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=37			,						layer_norm_eps=1e-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_000			,						)
         return CLIPTextModelWithProjection(UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         torch.manual_seed(0					)

         __A       =						{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         __A       =						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
         __A       =						nn.Parameter(torch.ones(model.clip_std.shape					)					)
         return model




     @property
     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         torch.manual_seed(0					)
         __A       =						CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size			,						image_size=224			,						projection_dim=self.text_embedder_hidden_size			,						intermediate_size=37			,						num_attention_heads=4			,						num_channels=3			,						num_hidden_layers=5			,						patch_size=14			,						)

         __A       =						CLIPVisionModelWithProjection(UpperCamelCase_					)
         return model




     @property
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						CLIPImageProcessor(
             crop_size=224			,						do_center_crop=UpperCamelCase_			,						do_normalize=UpperCamelCase_			,						do_resize=UpperCamelCase_			,						image_mean=[0.48145466, 0.4578275, 0.40821073]			,						image_std=[0.26862954, 0.26130258, 0.27577711]			,						resample=3			,						size=224			,						)

         return image_processor




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.dummy_prior
         __A       =						self.dummy_image_encoder
         __A       =						self.dummy_text_encoder
         __A       =						self.dummy_tokenizer
         __A       =						self.dummy_image_processor

         __A       =						UnCLIPScheduler(
             variance_type="""fixed_small_log"""			,						prediction_type="""sample"""			,						num_train_timesteps=1_000			,						clip_sample=UpperCamelCase_			,						clip_sample_range=10.0			,						)

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

         return components




     def 		lowerCAmelCase_  (					self							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]=0					):


         """simple docstring"""



         if str(UpperCamelCase_					).startswith("""mps"""					):
             __A       =						torch.manual_seed(UpperCamelCase_					)
         else:
             __A       =						torch.Generator(device=UpperCamelCase_					).manual_seed(UpperCamelCase_					)
         __A       =						{
             """prompt""": """horse""",
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						"""cpu"""

         __A       =						self.get_dummy_components()

         __A       =						self.pipeline_class(**UpperCamelCase_					)
         __A       =						pipe.to(UpperCamelCase_					)

         pipe.set_progress_bar_config(disable=UpperCamelCase_					)

         __A       =						pipe(**self.get_dummy_inputs(UpperCamelCase_					)					)
         __A       =						output.image_embeds

         __A       =						pipe(
             **self.get_dummy_inputs(UpperCamelCase_					)			,						return_dict=UpperCamelCase_			,						)[0]

         __A       =						image[0, -10:]
         __A       =						image_from_tuple[0, -10:]

         assert image.shape == (1, 32)

         __A       =						np.array(
             [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]					)

         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 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						torch_device == """cpu"""
         __A       =						True
         __A       =						False

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





     @skip_mps
     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						torch_device == """cpu"""
         __A       =						False

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












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   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							List[str]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__a   :							Dict						= {
    "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   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




     def __init__(					self							:      List[Any]			,						UpperCamelCase_							:      Any=None			,						UpperCamelCase_							:      Tuple=None			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      Union[str, Any]="replace"			,						UpperCamelCase_							:      Any="<s>"			,						UpperCamelCase_							:      Tuple="</s>"			,						UpperCamelCase_							:      Optional[Any]="</s>"			,						UpperCamelCase_							:      Dict="<s>"			,						UpperCamelCase_							:      int="<unk>"			,						UpperCamelCase_							:      Optional[int]="<pad>"			,						UpperCamelCase_							:      str="<mask>"			,						UpperCamelCase_							:      Tuple=False			,						UpperCamelCase_							:      Optional[Any]=True			,						**UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         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_			,						)

         __A       =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
             __A       =						getattr(UpperCamelCase_			,						pre_tok_state.pop("""type"""					)					)
             __A       =						add_prefix_space
             __A       =						pre_tok_class(**UpperCamelCase_					)

         __A       =						add_prefix_space

         __A       =						"""post_processor"""
         __A       =						getattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)
         if tokenizer_component_instance:
             __A       =						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:
                 __A       =						tuple(state["""sep"""]					)
             if "cls" in state:
                 __A       =						tuple(state["""cls"""]					)

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

             if changes_to_apply:
                 __A       =						getattr(UpperCamelCase_			,						state.pop("""type"""					)					)
                 __A       =						component_class(**UpperCamelCase_					)
                 setattr(self.backend_tokenizer			,						UpperCamelCase_			,						UpperCamelCase_					)




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						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 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						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 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[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 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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]




