

import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=[]				):







							'''simple docstring'''






							snake_case_          =   size[0] - overlap_pixels * 2
							snake_case_          =   size[1] - overlap_pixels * 2
							for letter in ["l", "r"]:
														if letter in remove_borders:
																					size_x += overlap_pixels
							for letter in ["t", "b"]:
														if letter in remove_borders:
																					size_y += overlap_pixels
							snake_case_          =   np.ones((size_y, size_x)       ,							dtype=np.uinta				) * 255
							snake_case_          =   np.pad(UpperCamelCase__       ,							mode='linear_ramp'       ,							pad_width=UpperCamelCase__       ,							end_values=0				)

							if "l" in remove_borders:
														snake_case_          =   mask[:, overlap_pixels : mask.shape[1]]
							if "r" in remove_borders:
														snake_case_          =   mask[:, 0 : mask.shape[1] - overlap_pixels]
							if "t" in remove_borders:
														snake_case_          =   mask[overlap_pixels : mask.shape[0], :]
							if "b" in remove_borders:
														snake_case_          =   mask[0 : mask.shape[0] - overlap_pixels, :]
							return mask





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							return max(UpperCamelCase__       ,							min(UpperCamelCase__       ,							UpperCamelCase__				)				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							return (
							    clamp(rect[0]       ,							min[0]       ,							max[0]				),
							    clamp(rect[1]       ,							min[1]       ,							max[1]				),
							    clamp(rect[2]       ,							min[0]       ,							max[0]				),
							    clamp(rect[3]       ,							min[1]       ,							max[1]				),
							)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   list(UpperCamelCase__				)
							rect[0] -= overlap
							rect[1] -= overlap
							rect[2] += overlap
							rect[3] += overlap
							snake_case_          =   clamp_rect(UpperCamelCase__       ,							[0, 0]       ,							[image_size[0], image_size[1]]				)
							return rect





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   Image.new('RGB'       ,							(tile.size[0] + original_slice, tile.size[1])				)
							result.paste(
							    original_image.resize((tile.size[0], tile.size[1])       ,							Image.BICUBIC				).crop(
							        (slice_x, 0, slice_x + original_slice, tile.size[1])				)       ,							(0, 0)       ,							)
							result.paste(UpperCamelCase__       ,							(original_slice, 0)				)
							return result





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   (original_image_slice * 4, 0, tile.size[0], tile.size[1])
							snake_case_          =   tile.crop(UpperCamelCase__				)
							return tile





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   n % d
							return n - divisor







class 			lowercase    (	lowercase_      ):




				def __init__( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case = 350				,       ):
											super().__init__(
											    vae=snake_case				,       text_encoder=snake_case				,       tokenizer=snake_case				,       unet=snake_case				,       low_res_scheduler=snake_case				,       scheduler=snake_case				,       max_noise_level=snake_case				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       **snake_case	):
											torch.manual_seed(0	)
											snake_case_          =   (
											    min(image.size[0] - (tile_size + original_image_slice)				,       x * tile_size	),
											    min(image.size[1] - (tile_size + original_image_slice)				,       y * tile_size	),
											    min(image.size[0]				,       (x + 1) * tile_size	),
											    min(image.size[1]				,       (y + 1) * tile_size	),
											)
											snake_case_          =   add_overlap_rect(snake_case				,       snake_case				,       image.size	)
											snake_case_          =   image.crop(snake_case	)
											snake_case_          =   ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
											snake_case_          =   translated_slice_x - (original_image_slice / 2)
											snake_case_          =   max(0				,       snake_case	)
											snake_case_          =   squeeze_tile(snake_case				,       snake_case				,       snake_case				,       snake_case	)
											snake_case_          =   to_input.size
											snake_case_          =   to_input.resize((tile_size, tile_size)				,       Image.BICUBIC	)
											snake_case_          =   super(snake_case				,       self	).__call__(image=snake_case				,       **snake_case	).images[0]
											snake_case_          =   upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)				,       Image.BICUBIC	)
											snake_case_          =   unsqueeze_tile(snake_case				,       snake_case	)
											snake_case_          =   upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)				,       Image.BICUBIC	)
											snake_case_          =   []
											if x == 0:
																		remove_borders.append('l'	)
											elif crop_rect[2] == image.size[0]:
																		remove_borders.append('r'	)
											if y == 0:
																		remove_borders.append('t'	)
											elif crop_rect[3] == image.size[1]:
																		remove_borders.append('b'	)
											snake_case_          =   Image.fromarray(
											    make_transparency_mask(
											        (upscaled_tile.size[0], upscaled_tile.size[1])				,       tile_border * 4				,       remove_borders=snake_case	)				,       mode='L'				,       )
											final_image.paste(
											    snake_case				,       (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)				,       snake_case	)




				@torch.no_grad()
				def __call__( self				,       snake_case				,       snake_case				,       snake_case = 75				,       snake_case = 9.0				,       snake_case = 50				,       snake_case = None				,       snake_case = 1				,       snake_case = 0.0				,       snake_case = None				,       snake_case = None				,       snake_case = None				,       snake_case = 1				,       snake_case = 128				,       snake_case = 32				,       snake_case = 32				,       ):
											snake_case_          =   Image.new('RGB'				,       (image.size[0] * 4, image.size[1] * 4)	)
											snake_case_          =   math.ceil(image.size[0] / tile_size	)
											snake_case_          =   math.ceil(image.size[1] / tile_size	)
											snake_case_          =   tcx * tcy
											snake_case_          =   0
											for y in range(snake_case	):
																		for x in range(snake_case	):
																									self._process_tile(
																									    snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       prompt=snake_case				,       num_inference_steps=snake_case				,       guidance_scale=snake_case				,       noise_level=snake_case				,       negative_prompt=snake_case				,       num_images_per_prompt=snake_case				,       eta=snake_case				,       generator=snake_case				,       latents=snake_case				,       )
																									current_count += 1
																									if callback is not None:
																																callback({'progress': current_count / total_tile_count, 'image': final_image}	)
											return final_image





def  __lowerCamelCase     (			):







							'''simple docstring'''






							snake_case_          =   'stabilityai/stable-diffusion-x4-upscaler'
							snake_case_          =   StableDiffusionTiledUpscalePipeline.from_pretrained(UpperCamelCase__       ,							revision='fp16'       ,							torch_dtype=torch.floataa				)
							snake_case_          =   pipe.to('cuda'				)
							snake_case_          =   Image.open('../../docs/source/imgs/diffusers_library.jpg'				)

							def callback(UpperCamelCase__				):
														print(F'''progress: {obj["progress"]:.4f}'''				)
														obj["image"].save('diffusers_library_progress.jpg'				)

							snake_case_          =   pipe(image=UpperCamelCase__       ,							prompt='Black font, white background, vector'       ,							noise_level=40       ,							callback=UpperCamelCase__				)
							final_image.save('diffusers_library.jpg'				)


if __name__ == "__main__":
					main()







import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=99				,       snake_case=13				,       snake_case=7				,       snake_case=9				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=37				,       snake_case=8				,       snake_case=0.1				,       snake_case=0.0_02				,       snake_case=1				,       snake_case=0				,       snake_case=0				,       snake_case=None				,       snake_case=None				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   encoder_seq_length
											snake_case_          =   decoder_seq_length
											# For common tests
											snake_case_          =   self.decoder_seq_length
											snake_case_          =   is_training
											snake_case_          =   use_attention_mask
											snake_case_          =   use_labels
											snake_case_          =   vocab_size
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   d_ff
											snake_case_          =   relative_attention_num_buckets
											snake_case_          =   dropout_rate
											snake_case_          =   initializer_factor
											snake_case_          =   eos_token_id
											snake_case_          =   pad_token_id
											snake_case_          =   decoder_start_token_id
											snake_case_          =   None
											snake_case_          =   decoder_layers




				def    a							( self	):
											return TaConfig.from_pretrained('google/umt5-base'	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       snake_case=None				,       ):
											if attention_mask is None:
																		snake_case_          =   input_ids.ne(config.pad_token_id	)
											if decoder_attention_mask is None:
																		snake_case_          =   decoder_input_ids.ne(config.pad_token_id	)
											if head_mask is None:
																		snake_case_          =   torch.ones(config.num_hidden_layers				,       config.num_attention_heads				,       device=snake_case	)
											if decoder_head_mask is None:
																		snake_case_          =   torch.ones(config.num_decoder_layers				,       config.num_attention_heads				,       device=snake_case	)
											if cross_attn_head_mask is None:
																		snake_case_          =   torch.ones(
																		    config.num_decoder_layers				,       config.num_attention_heads				,       device=snake_case	)
											return {
											    "input_ids": input_ids,
											    "decoder_input_ids": decoder_input_ids,
											    "attention_mask": attention_mask,
											    "decoder_attention_mask": decoder_attention_mask,
											    "head_mask": head_mask,
											    "decoder_head_mask": decoder_head_mask,
											    "cross_attn_head_mask": cross_attn_head_mask,
											}




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.encoder_seq_length]				,       self.vocab_size	)
											snake_case_          =   ids_tensor([self.batch_size, self.decoder_seq_length]				,       self.vocab_size	)

											# we need to clamp the input ids here to avoid having pad token in between
											# this is because for NllbMoe the position_ids are prepared such that
											# all pad tokens have pos id = 2 and rest are between 2..seq_length
											# and the seq_length here is seq_length - num_pad_tokens
											# but when using past, there is no way of knowing if the past input ids had
											# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
											# position_ids being off by num_pad_tokens in past input
											snake_case_          =   input_ids.clamp(self.pad_token_id + 1	)
											snake_case_          =   decoder_input_ids.clamp(self.pad_token_id + 1	)

											snake_case_          =   self.get_config()
											snake_case_          =   config.num_attention_heads
											snake_case_          =   self.prepare_inputs_dict(snake_case				,       snake_case				,       snake_case	)
											return config, input_dict




				def    a							( self	):
											snake_case_    ,   snake_case_          =   self.prepare_config_and_inputs()
											return config, inputs_dict




				def    a							( self	):
											return TaConfig(
											    vocab_size=166				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )




				def    a							( self	):
											return TaConfig(
											    vocab_size=self.vocab_size				,       d_model=self.hidden_size				,       d_ff=self.d_ff				,       d_kv=self.hidden_size // self.num_attention_heads				,       num_layers=self.num_hidden_layers				,       num_decoder_layers=self.decoder_layers				,       num_heads=self.num_attention_heads				,       relative_attention_num_buckets=self.relative_attention_num_buckets				,       dropout_rate=self.dropout_rate				,       initializer_factor=self.initializer_factor				,       eos_token_id=self.eos_token_id				,       bos_token_id=self.pad_token_id				,       pad_token_id=self.pad_token_id				,       decoder_start_token_id=self.decoder_start_token_id				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(
											    input_ids=snake_case				,       decoder_input_ids=snake_case				,       attention_mask=snake_case				,       decoder_attention_mask=snake_case				,       )
											snake_case_          =   model(input_ids=snake_case				,       decoder_input_ids=snake_case	)
											snake_case_          =   result.last_hidden_state
											snake_case_          =   result.past_key_values
											snake_case_          =   result.encoder_last_hidden_state

											self.parent.assertEqual(encoder_output.size()				,       (self.batch_size, self.encoder_seq_length, self.hidden_size)	)
											self.parent.assertEqual(decoder_output.size()				,       (self.batch_size, self.decoder_seq_length, self.hidden_size)	)
											# There should be `num_layers` key value embeddings stored in decoder_past
											self.parent.assertEqual(len(snake_case	)				,       config.num_layers	)
											# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
											self.parent.assertEqual(len(decoder_past[0]	)				,       4	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	).get_decoder().to(snake_case	).eval()
											# first forward pass
											snake_case_          =   model(snake_case				,       use_cache=snake_case	)
											snake_case_          =   model(snake_case	)
											snake_case_          =   model(snake_case				,       use_cache=snake_case	)

											self.parent.assertTrue(len(snake_case	) == len(snake_case	)	)
											self.parent.assertTrue(len(snake_case	) == len(snake_case	) + 1	)

											snake_case_    ,   snake_case_          =   outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											snake_case_          =   ids_tensor((self.batch_size, 1)				,       config.vocab_size	)

											# append to next input_ids and
											snake_case_          =   torch.cat([input_ids, next_tokens]				,       dim=-1	)

											snake_case_          =   model(snake_case	)['last_hidden_state']
											snake_case_          =   model(snake_case				,       past_key_values=snake_case	)['last_hidden_state']

											# select random slice
											snake_case_          =   ids_tensor((1,)				,       output_from_past.shape[-1]	).item()
											snake_case_          =   output_from_no_past[:, -1, random_slice_idx].detach()
											snake_case_          =   output_from_past[:, 0, random_slice_idx].detach()

											# test that outputs are equal for slice
											self.parent.assertTrue(torch.allclose(snake_case				,       snake_case				,       atol=1e-3	)	)




				def    a							( self				,       snake_case				,       snake_case				,       ):
											snake_case_          =   UMTaModel(config=snake_case	).to(snake_case	).half().eval()
											snake_case_          =   model(**snake_case	)['last_hidden_state']
											self.parent.assertFalse(torch.isnan(snake_case	).any().item()	)







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      (
				    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
				)
				__SCREAMING_SNAKE_CASE :			int	      =      (UMTaForConditionalGeneration,) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      (
				    {
				        '''conversational''': UMTaForConditionalGeneration,
				        '''feature-extraction''': UMTaModel,
				        '''summarization''': UMTaForConditionalGeneration,
				        '''text2text-generation''': UMTaForConditionalGeneration,
				        '''translation''': UMTaForConditionalGeneration,
				        '''question-answering''': UMTaForQuestionAnswering,
				    }
				    if is_torch_available()
				    else {}
				)
				__SCREAMING_SNAKE_CASE :			List[str]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      False
				__SCREAMING_SNAKE_CASE :			int	      =      False
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      True
				__SCREAMING_SNAKE_CASE :			Any	      =      True
				# The small UMT5 model needs higher percentages for CPU/MP tests
				__SCREAMING_SNAKE_CASE :			List[str]	      =      [0.8, 0.9]




				def    a							( self	):
											snake_case_          =   UMTaModelTester(self	)




				@unittest.skip('Test has a segmentation fault on torch 1.8.0'	)
				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											snake_case_          =   UMTaModel(config_and_inputs[0]	).to(snake_case	)
											with tempfile.TemporaryDirectory() as tmpdirname:
																		torch.onnx.export(
																		    snake_case				,       (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])				,       F'''{tmpdirname}/t5_test.onnx'''				,       export_params=snake_case				,       opset_version=9				,       input_names=['input_ids', 'decoder_input_ids']				,       )




				@unittest.skipIf(torch_device == 'cpu'				,       'Cant do half precision'	)
				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model_fpaa_forward(*snake_case	)




				def    a							( self	):
											snake_case_          =   ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											snake_case_          =   config_and_inputs[0]
											snake_case_          =   UMTaForConditionalGeneration(snake_case	).eval()
											model.to(snake_case	)

											snake_case_          =   {
											    'head_mask': torch.zeros(config.num_layers				,       config.num_heads				,       device=snake_case	),
											    'decoder_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=snake_case	),
											    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers				,       config.num_heads				,       device=snake_case	),
											}

											for attn_name, (name, mask) in zip(snake_case				,       head_masking.items()	):
																		snake_case_          =   {name: mask}
																		# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
																		if name == "head_mask":
																									snake_case_          =   torch.ones(
																									    config.num_decoder_layers				,       config.num_heads				,       device=snake_case	)

																		snake_case_          =   model.generate(
																		    config_and_inputs[1]['input_ids']				,       num_beams=1				,       max_length=3				,       output_attentions=snake_case				,       return_dict_in_generate=snake_case				,       **snake_case				,       )
																		# We check the state of decoder_attentions and cross_attentions just from the last step
																		snake_case_          =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
																		self.assertEqual(sum([w.sum().item() for w in attn_weights]	)				,       0.0	)




				@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'	)
				def    a							( self	):
											pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase    (	unittest.TestCase      ):




				@slow
				@unittest.skip(
				    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'	)
				def    a							( self	):
											snake_case_          =   UMTaForConditionalGeneration.from_pretrained('google/umt5-small'				,       return_dict=snake_case	).to(snake_case	)
											snake_case_          =   AutoTokenizer.from_pretrained('google/umt5-small'				,       use_fast=snake_case				,       legacy=snake_case	)
											snake_case_          =   [
											    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
											    'No se como puedo <extra_id_0>.',
											    'This is the reason why we <extra_id_0> them.',
											    'The <extra_id_0> walks in <extra_id_1>, seats',
											    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
											]
											snake_case_          =   tokenizer(snake_case				,       return_tensors='pt'				,       padding=snake_case	).input_ids
											# fmt: off
											snake_case_          =   torch.tensor(
											    [
											        [ 3_8530, 21_0703, 25_6299, 1410, 25_6298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   826, 321, 671, 2_5922, 25_6299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [  1460, 339, 312, 1_9014, 1_0620, 758, 25_6299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
											        [   517, 25_6299, 1_4869, 281, 301, 25_6298, 275, 11_9983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
											        [   320, 25_6299, 1_4869, 281, 2234, 289, 2275, 333,6_1391, 289, 25_6298, 543, 25_6297, 16_8714, 329, 25_6296,274, 1],
											    ]	)
											# fmt: on
											torch.testing.assert_allclose(snake_case				,       snake_case	)

											snake_case_          =   model.generate(input_ids.to(snake_case	)	)
											snake_case_          =   [
											    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
											    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
											]
											snake_case_          =   tokenizer.batch_decode(snake_case	)
											self.assertEqual(snake_case				,       snake_case	)




