

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 						__magic_name__					(							unittest.TestCase				):






     @property
     def 	UpperCAmelCase_				(				self	)->     List[Any]:
         torch.manual_seed(0	)
         UpperCamelCase_			  =						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	)->     str:
         UpperCamelCase_			  =						self.dummy_uncond_unet
         UpperCamelCase_			  =						KarrasVeScheduler()

         UpperCamelCase_			  =						KarrasVePipeline(unet=_lowercase		,						scheduler=_lowercase	)
         pipe.to(_lowercase	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						torch.manual_seed(0	)
         UpperCamelCase_			  =						pipe(num_inference_steps=2		,						generator=_lowercase		,						output_type="numpy"	).images

         UpperCamelCase_			  =						torch.manual_seed(0	)
         UpperCamelCase_			  =						pipe(num_inference_steps=2		,						generator=_lowercase		,						output_type="numpy"		,						return_dict=_lowercase	)[0]

         UpperCamelCase_			  =						image[0, -3:, -3:, -1]
         UpperCamelCase_			  =						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCamelCase_			  =						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 						__magic_name__					(							unittest.TestCase				):






     def 	UpperCAmelCase_				(				self	)->     Dict:
         UpperCamelCase_			  =						"google/ncsnpp-celebahq-256"
         UpperCamelCase_			  =						UNetaDModel.from_pretrained(_lowercase	)
         UpperCamelCase_			  =						KarrasVeScheduler()

         UpperCamelCase_			  =						KarrasVePipeline(unet=_lowercase		,						scheduler=_lowercase	)
         pipe.to(_lowercase	)
         pipe.set_progress_bar_config(disable=_lowercase	)

         UpperCamelCase_			  =						torch.manual_seed(0	)
         UpperCamelCase_			  =						pipe(num_inference_steps=20		,						generator=_lowercase		,						output_type="numpy"	).images

         UpperCamelCase_			  =						image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         UpperCamelCase_			  =						np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]	)

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



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
