



'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				__lowerCAmelCase(							unittest.TestCase      ):






			@property
			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:str										=      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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      'cpu'
					SCREAMING_SNAKE_CASE_						:int										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:str										=      DDPMScheduler()

					SCREAMING_SNAKE_CASE_						:Tuple										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:str										=      torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'      ,       return_dict=SCREAMING_SNAKE_CASE	)[0]

					SCREAMING_SNAKE_CASE_						:Tuple										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Any										=      np.array(
					    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]	)

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.dummy_uncond_unet
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DDPMScheduler(prediction_type='sample'	)

					SCREAMING_SNAKE_CASE_						:Any										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Any										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:int										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       num_inference_steps=2      ,       output_type='numpy'	)[0]

					SCREAMING_SNAKE_CASE_						:List[Any]										=      image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE_						:Dict										=      image_eps[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Tuple										=      1E-2 if torch_device != 'mps' else 3E-2
					assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance




@slow
@require_torch_gpu
class 				__lowerCAmelCase(							unittest.TestCase      ):






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      'google/ddpm-cifar10-32'

					SCREAMING_SNAKE_CASE_						:Any										=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DDPMPipeline(unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE	)
					ddpm.to(SCREAMING_SNAKE_CASE	)
					ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Tuple										=      torch.manual_seed(0	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      ddpm(generator=SCREAMING_SNAKE_CASE      ,       output_type='numpy'	).images

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					SCREAMING_SNAKE_CASE_						:Dict										=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]	)
					assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


SCREAMING_SNAKE_CASE__     : List[Any]				=   logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__     : Optional[int]				=   {
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__     : Tuple				=   {
    """microsoft/layoutlm-base-uncased""": 5_12,
    """microsoft/layoutlm-large-uncased""": 5_12,
}

SCREAMING_SNAKE_CASE__     : List[Any]				=   {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[str]		=      VOCAB_FILES_NAMES
			__snake_case  :		Dict		=      PRETRAINED_VOCAB_FILES_MAP
			__snake_case  :		Optional[int]		=      PRETRAINED_INIT_CONFIGURATION
			__snake_case  :		Any		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case  :		str		=      LayoutLMTokenizer






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




					"""simple docstring"""







					super().__init__(
					    SCREAMING_SNAKE_CASE      ,       tokenizer_file=SCREAMING_SNAKE_CASE      ,       do_lower_case=SCREAMING_SNAKE_CASE      ,       unk_token=SCREAMING_SNAKE_CASE      ,       sep_token=SCREAMING_SNAKE_CASE      ,       pad_token=SCREAMING_SNAKE_CASE      ,       cls_token=SCREAMING_SNAKE_CASE      ,       mask_token=SCREAMING_SNAKE_CASE      ,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE      ,       strip_accents=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE      ,       )

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

					SCREAMING_SNAKE_CASE_						:List[str]										=      do_lower_case






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      [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 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[List[int]] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [self.sep_token_id]
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [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 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE      ,       name=SCREAMING_SNAKE_CASE	)
					return tuple(SCREAMING_SNAKE_CASE	)
