





'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case_     :       Tuple						=  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

snake_case_     :       int						=  {
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

snake_case_     :       Dict						=  {
    "unc-nlp/lxmert-base-uncased": 5_12,
}

snake_case_     :       Dict						=  {
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}


class 					__a      (lowerCamelCase     ):
							__a					:   List[str]									=       VOCAB_FILES_NAMES
							__a					:   int									=       PRETRAINED_VOCAB_FILES_MAP
							__a					:   Optional[Any]									=       PRETRAINED_INIT_CONFIGURATION
							__a					:   int									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a					:   List[Any]									=       LxmertTokenizer

							def __init__(							self							:     List[str]       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Tuple=None       ,     __magic_name__							:     Tuple=True       ,     __magic_name__							:     str="[UNK]"       ,     __magic_name__							:     Optional[int]="[SEP]"       ,     __magic_name__							:     str="[PAD]"       ,     __magic_name__							:     Optional[Any]="[CLS]"       ,     __magic_name__							:     Optional[int]="[MASK]"       ,     __magic_name__							:     Any=True       ,     __magic_name__							:     Any=None       ,     **__magic_name__							:     List[Any]       ,     )					->       Dict:







													"""simple docstring"""
													super().__init__(
													    __magic_name__       ,     tokenizer_file=__magic_name__       ,     do_lower_case=__magic_name__       ,     unk_token=__magic_name__       ,     sep_token=__magic_name__       ,     pad_token=__magic_name__       ,     cls_token=__magic_name__       ,     mask_token=__magic_name__       ,     tokenize_chinese_chars=__magic_name__       ,     strip_accents=__magic_name__       ,     **__magic_name__       ,     )

													UpperCAmelCase_			:      Union[str, Any]     						=  json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
													if (
													    normalizer_state.get('''lowercase'''       ,     __magic_name__   ) != do_lower_case
													    or normalizer_state.get('''strip_accents'''       ,     __magic_name__   ) != strip_accents
													    or normalizer_state.get('''handle_chinese_chars'''       ,     __magic_name__   ) != tokenize_chinese_chars
													):
																			UpperCAmelCase_			:      Any     						=  getattr(__magic_name__       ,     normalizer_state.pop('''type'''   )   )
																			UpperCAmelCase_			:      str     						=  do_lower_case
																			UpperCAmelCase_			:      Tuple     						=  strip_accents
																			UpperCAmelCase_			:      Tuple     						=  tokenize_chinese_chars
																			UpperCAmelCase_			:      Any     						=  normalizer_class(**__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  do_lower_case

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[Any]=None   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

													if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

													return output

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[int]       ,     __magic_name__							:     Optional[List[int]] = None   )					->       List[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  [self.sep_token_id]
													UpperCAmelCase_			:      List[str]     						=  [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   ) * [0] + len(token_ids_a + sep   ) * [1]



							def     UpperCAmelCase__	(							self							:     Tuple       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[str] = None   )					->       Tuple[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  self._tokenizer.model.save(__magic_name__       ,     name=__magic_name__   )
													return tuple(__magic_name__   )












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					__a      (unittest.TestCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[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     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  np.array([0.0, 1.0, 0.0, 0.0, 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 					__a      (unittest.TestCase     ):

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







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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





