


"""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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@property
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								torch.manual_seed(0)
								snake_case__  :						Tuple		=      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)   ->	int:







								'''simple docstring'''




								snake_case__  :						int		=      "cpu"
								snake_case__  :						Tuple		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler()

								snake_case__  :						List[Any]		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Optional[int]		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Any		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy"	,					return_dict=lowerCamelCase__)[0]

								snake_case__  :						Optional[Any]		=      image[0, -3:, -3:, -1]
								snake_case__  :						int		=      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, 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        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler(prediction_type="sample")

								snake_case__  :						Any		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      torch.manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.manual_seed(0)
								snake_case__  :						str		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy")[0]

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

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						str		=      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 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Any		=      "google/ddpm-cifar10-32"

								snake_case__  :						Any		=      UNetaDModel.from_pretrained(lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      DDPMScheduler.from_pretrained(lowerCamelCase__)

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

								snake_case__  :						Tuple		=      torch.manual_seed(0)
								snake_case__  :						Union[str, Any]		=      ddpm(generator=lowerCamelCase__	,					output_type="numpy").images

								snake_case__  :						int		=      image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, Any]		=      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













import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class __lowerCamelCase					( snake_case__	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= GPTNeoXJapaneseTokenizer
	UpperCamelCase__								= False
	UpperCamelCase__								= {"do_clean_text": False, "add_prefix_space": False}

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase							   =     [
							    'こん',
							    'こんに',
							    'にちは',
							    'ばんは',
							    '世界,㔺界',
							    '、',
							    '。',
							    '<BR>',
							    '<SP>',
							    '<TAB>',
							    '<URL>',
							    '<EMAIL>',
							    '<TEL>',
							    '<DATE>',
							    '<PRICE>',
							    '<BLOCK>',
							    '<KIGOU>',
							    '<U2000U2BFF>',
							    '<|emoji1|>',
							    '<unk>',
							    '<|startoftext|>',
							    '<|endoftext|>',
							]
							_UpperCAmelCase							   =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							_UpperCAmelCase							   =     {'unk_token': '<unk>'}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['vocab_file']       )
							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['emoji_file']       )
							with open(self.vocab_file			,       'w'			,       encoding='utf-8'       ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
							with open(self.emoji_file			,       'w'       ) as emoji_writer:
													emoji_writer.write(json.dumps(UpperCAmelCase       )       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							kwargs.update(self.special_tokens_map       )
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_input_output_texts(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode(UpperCAmelCase			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.decode(UpperCAmelCase			,       clean_up_tokenization_spaces=UpperCAmelCase       )
							return text, ids

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()

							# Testing tokenization
							_UpperCAmelCase							   =     'こんにちは、世界。　こんばんは、㔺界。'
							_UpperCAmelCase							   =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							_UpperCAmelCase							   =     tokenizer.tokenize(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids without special tokens
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids with special tokens
							_UpperCAmelCase							   =     tokens + [tokenizer.unk_token]
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

							_UpperCAmelCase							   =     tokenizer.encode('ありがとう。'			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode('どういたしまして。'			,       add_special_tokens=UpperCAmelCase       )

							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,       UpperCAmelCase       )

							assert encoded_sentence == ids_a
							assert encoded_pair == ids_a + ids_a

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass
