






"""simple docstring"""

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase    			=       logging.get_logger(__name__)

lowercase    			=       '''▁'''

lowercase    			=       {'''vocab_file''': '''sentencepiece.bpe.model'''}

lowercase    			=       {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowercase    			=       {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}

class 						A_							(      snake_case_  ):
				UpperCAmelCase__        =				VOCAB_FILES_NAMES
				UpperCAmelCase__        =				PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__        =				['''input_ids''', '''attention_mask''']







				def __init__(							self							:						Dict						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						List[str]="<s>"						,    __lowerCamelCase							:						Optional[int]="</s>"						,    __lowerCamelCase							:						Tuple="</s>"						,    __lowerCamelCase							:						Union[str, Any]="<s>"						,    __lowerCamelCase							:						Union[str, Any]="<unk>"						,    __lowerCamelCase							:						List[Any]="<pad>"						,    __lowerCamelCase							:						str="<mask>"						,    __lowerCamelCase							:						Optional[Dict[str, Any]] = None						,    **__lowerCamelCase							:						Optional[int]						,    )				->					None:
										# Mask token behave like a normal word, i.e. include the space before it
										__magic_name__            =     AddedToken(__lowerCamelCase						,    lstrip=__lowerCamelCase						,    rstrip=__lowerCamelCase	) if isinstance(__lowerCamelCase						,    __lowerCamelCase	) else mask_token

										__magic_name__            =     {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=__lowerCamelCase						,    eos_token=__lowerCamelCase						,    unk_token=__lowerCamelCase						,    sep_token=__lowerCamelCase						,    cls_token=__lowerCamelCase						,    pad_token=__lowerCamelCase						,    mask_token=__lowerCamelCase						,    sp_model_kwargs=self.sp_model_kwargs						,    **__lowerCamelCase						,    )

										__magic_name__            =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__lowerCamelCase	)	)
										__magic_name__            =     vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										__magic_name__            =     {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										__magic_name__            =     1

										__magic_name__            =     len(self.sp_model	) + self.fairseq_offset
										__magic_name__            =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}







				def __getstate__(							self							:						Any	)				->					int:
										__magic_name__            =     self.__dict__.copy()
										__magic_name__            =     None
										__magic_name__            =     self.sp_model.serialized_model_proto()
										return state







				def __setstate__(							self							:						Union[str, Any]						,    __lowerCamelCase							:						List[Any]	)				->					Dict:
										__magic_name__            =     d

										# for backward compatibility
										if not hasattr(self						,    "sp_model_kwargs"	):
																__magic_name__            =     {}

										__magic_name__            =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)







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

										if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__magic_name__            =     [self.cls_token_id]
										__magic_name__            =     [self.sep_token_id]
										return cls + token_ids_a + sep + sep + token_ids_a + sep







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						List[int]						,    __lowerCamelCase							:						Optional[List[int]] = None						,    __lowerCamelCase							:						bool = False	)				->					List[int]:

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=__lowerCamelCase						,    token_ids_a=__lowerCamelCase						,    already_has_special_tokens=__lowerCamelCase	)

										if token_ids_a is None:
																return [1] + ([0] * len(__lowerCamelCase	)) + [1]
										return [1] + ([0] * len(__lowerCamelCase	)) + [1, 1] + ([0] * len(__lowerCamelCase	)) + [1]







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						List[int]						,    __lowerCamelCase							:						Optional[List[int]] = None	)				->					List[int]:
										__magic_name__            =     [self.sep_token_id]
										__magic_name__            =     [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]







				@property
				def     _snake_case					(							self							:						str	)				->					Union[str, Any]:
										return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token







				def     _snake_case					(							self							:						Tuple	)				->					Union[str, Any]:
										__magic_name__            =     {self.convert_ids_to_tokens(__lowerCamelCase	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab







				def     _snake_case					(							self							:						Any						,    __lowerCamelCase							:						str	)				->					List[str]:
										return self.sp_model.encode(__lowerCamelCase						,    out_type=__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						str	)				->					str:
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										__magic_name__            =     self.sp_model.PieceToId(__lowerCamelCase	)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







				def     _snake_case					(							self							:						str						,    __lowerCamelCase							:						Optional[Any]	)				->					Union[str, Any]:
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset	)







				def     _snake_case					(							self							:						Union[str, Any]						,    __lowerCamelCase							:						Any	)				->					Tuple:
										__magic_name__            =     "".join(__lowerCamelCase	).replace(__lowerCamelCase						,    " "	).strip()
										return out_string







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Optional[str] = None	)				->					Tuple[str]:
										if not os.path.isdir(__lowerCamelCase	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__magic_name__            =     os.path.join(
										    __lowerCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

										if os.path.abspath(self.vocab_file	) != os.path.abspath(__lowerCamelCase	) and os.path.isfile(self.vocab_file	):
																copyfile(self.vocab_file						,    __lowerCamelCase	)
										elif not os.path.isfile(self.vocab_file	):
																with open(__lowerCamelCase						,    "wb"	) as fi:
																						__magic_name__            =     self.sp_model.serialized_model_proto()
																						fi.write(__lowerCamelCase	)

										return (out_vocab_file,)











"""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 						A_							(      snake_case_					,							unittest.TestCase  ):
				UpperCAmelCase__        =				KandinskyVaaPriorPipeline
				UpperCAmelCase__        =				['''prompt''']
				UpperCAmelCase__        =				['''prompt''', '''negative_prompt''']
				UpperCAmelCase__        =				[
				    '''num_images_per_prompt''',
				    '''generator''',
				    '''num_inference_steps''',
				    '''latents''',
				    '''negative_prompt''',
				    '''guidance_scale''',
				    '''output_type''',
				    '''return_dict''',
				]
				UpperCAmelCase__        =				False







				@property
				def     _snake_case					(							self							:						Dict	)				->					Dict:
										return 3_2







				@property
				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										return 3_2







				@property
				def     _snake_case					(							self							:						Dict	)				->					Optional[int]:
										return self.time_input_dim







				@property
				def     _snake_case					(							self							:						List[Any]	)				->					str:
										return self.time_input_dim * 4







				@property
				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										return 1_0_0







				@property
				def     _snake_case					(							self							:						List[str]	)				->					Tuple:
										__magic_name__            =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer







				@property
				def     _snake_case					(							self							:						Optional[int]	)				->					Optional[int]:
										torch.manual_seed(0	)
										__magic_name__            =     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-05						,    num_attention_heads=4						,    num_hidden_layers=5						,    pad_token_id=1						,    vocab_size=1_0_0_0						,    )
										return CLIPTextModelWithProjection(__lowerCamelCase	)







				@property
				def     _snake_case					(							self							:						List[str]	)				->					Union[str, Any]:
										torch.manual_seed(0	)

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

										__magic_name__            =     PriorTransformer(**__lowerCamelCase	)
										# 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
										__magic_name__            =     nn.Parameter(torch.ones(model.clip_std.shape	)	)
										return model







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					Union[str, Any]:
										torch.manual_seed(0	)
										__magic_name__            =     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						,    )

										__magic_name__            =     CLIPVisionModelWithProjection(__lowerCamelCase	)
										return model







				@property
				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										__magic_name__            =     CLIPImageProcessor(
										    crop_size=2_2_4						,    do_center_crop=__lowerCamelCase						,    do_normalize=__lowerCamelCase						,    do_resize=__lowerCamelCase						,    image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]						,    image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,    resample=3						,    size=2_2_4						,    )

										return image_processor







				def     _snake_case					(							self							:						List[Any]	)				->					Optional[Any]:
										__magic_name__            =     self.dummy_prior
										__magic_name__            =     self.dummy_image_encoder
										__magic_name__            =     self.dummy_text_encoder
										__magic_name__            =     self.dummy_tokenizer
										__magic_name__            =     self.dummy_image_processor

										__magic_name__            =     UnCLIPScheduler(
										    variance_type="fixed_small_log"						,    prediction_type="sample"						,    num_train_timesteps=1_0_0_0						,    clip_sample=__lowerCamelCase						,    clip_sample_range=10.0						,    )

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

										return components







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						List[Any]						,    __lowerCamelCase							:						str=0	)				->					Optional[int]:
										if str(__lowerCamelCase	).startswith("mps"	):
																__magic_name__            =     torch.manual_seed(__lowerCamelCase	)
										else:
																__magic_name__            =     torch.Generator(device=__lowerCamelCase	).manual_seed(__lowerCamelCase	)
										__magic_name__            =     {
										    "prompt": "horse",
										    "generator": generator,
										    "guidance_scale": 4.0,
										    "num_inference_steps": 2,
										    "output_type": "np",
										}
										return inputs







				def     _snake_case					(							self							:						Optional[Any]	)				->					Optional[Any]:
										__magic_name__            =     "cpu"

										__magic_name__            =     self.get_dummy_components()

										__magic_name__            =     self.pipeline_class(**__lowerCamelCase	)
										__magic_name__            =     pipe.to(__lowerCamelCase	)

										pipe.set_progress_bar_config(disable=__lowerCamelCase	)

										__magic_name__            =     pipe(**self.get_dummy_inputs(__lowerCamelCase	)	)
										__magic_name__            =     output.image_embeds

										__magic_name__            =     pipe(
										    **self.get_dummy_inputs(__lowerCamelCase	)						,    return_dict=__lowerCamelCase						,    )[0]

										__magic_name__            =     image[0, -1_0:]
										__magic_name__            =     image_from_tuple[0, -1_0:]

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

										__magic_name__            =     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     _snake_case					(							self							:						Dict	)				->					List[str]:
										__magic_name__            =     torch_device == "cpu"
										__magic_name__            =     True
										__magic_name__            =     False

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







				@skip_mps
				def     _snake_case					(							self							:						Tuple	)				->					Tuple:
										__magic_name__            =     torch_device == "cpu"
										__magic_name__            =     False

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



