

'''simple docstring'''

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class lowerCamelCase   (    unittest.TestCase				):

				@property
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					1
					snake_case							=					3
					snake_case							=					8
					snake_case							=					8

					snake_case							=					torch.rand((batch_size, num_channels, height, width) )

					return sample

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							List[str]:
					if isinstance(image[0],						PIL.Image.Image ):
						return np.stack([np.array(lowercase_ ) for i in image],						axis=0 )
					elif isinstance(lowercase_,						torch.Tensor ):
						return image.cpu().numpy().transpose(0,						2,						3,						1 )
					return image

				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample
					snake_case							=					self.to_np(lowercase_ )

					for output_type in ["pt", "np", "pil"]:
						snake_case							=					image_processor.postprocess(
						    image_processor.preprocess(lowercase_ ),						output_type=lowercase_,						)
						snake_case							=					self.to_np(lowercase_ )
						snake_case							=					(input_np * 255).round() if output_type == 'pil' else input_np
						assert (
						    np.abs(in_np - out_np ).max() < 1E-6
						), F'''decoded output does not match input for output_type {output_type}'''

				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )
					snake_case							=					self.dummy_sample.cpu().numpy().transpose(0,						2,						3,						1 )

					for output_type in ["pt", "np", "pil"]:
						snake_case							=					image_processor.postprocess(image_processor.preprocess(lowercase_ ),						output_type=lowercase_ )

						snake_case							=					self.to_np(lowercase_ )
						snake_case							=					(input_np * 255).round() if output_type == 'pil' else input_np
						assert (
						    np.abs(in_np - out_np ).max() < 1E-6
						), F'''decoded output does not match input for output_type {output_type}'''

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample.cpu().numpy().transpose(0,						2,						3,						1 )
					snake_case							=					image_processor.numpy_to_pil(lowercase_ )

					for output_type in ["pt", "np", "pil"]:
						snake_case							=					image_processor.postprocess(image_processor.preprocess(lowercase_ ),						output_type=lowercase_ )
						for i, o in zip(lowercase_,						lowercase_ ):
							snake_case							=					np.array(lowercase_ )
							snake_case							=					self.to_np(lowercase_ ) if output_type == 'pil' else (self.to_np(lowercase_ ) * 255).round()
							assert (
							    np.abs(in_np - out_np ).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample
					snake_case							=					input_pt_ad.squeeze(0 )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)
					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					self.to_np(self.dummy_sample )
					snake_case							=					input_np_ad.squeeze(0 )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)
					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					assert np.abs(out_pt_ad - out_pt_ad ).max() < 1E-6
					assert np.abs(out_np_ad - out_np_ad ).max() < 1E-6






				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					VaeImageProcessor(do_resize=lowercase_,						do_normalize=lowercase_ )

					snake_case							=					self.dummy_sample
					snake_case							=					list(lowercase_ )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					self.to_np(self.dummy_sample )
					list(lowercase_ )

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					snake_case							=					image_processor.postprocess(
					    image_processor.preprocess(lowercase_ ),						output_type='np',						)

					assert np.abs(out_pt_ad - out_pt_list ).max() < 1E-6
					assert np.abs(out_np_ad - out_np_list ).max() < 1E-6







'''simple docstring'''







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  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							GPTNeoXJapaneseTokenizer
	lowercase__	    =							False
	lowercase__	    =							{"do_clean_text": False, "add_prefix_space": False}
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					super().setUp()

					__a       :     Optional[int]								=	[
					    """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|>""",
					]
					__a       :     Any								=	{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
					__a       :     List[str]								=	{"""unk_token""": """<unk>"""}

					__a       :     Any								=	os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES["""vocab_file"""]			)
					__a       :     Tuple								=	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 __lowerCamelCase    (				self  ,    **__UpperCamelCase			):



					'''simple docstring'''






					kwargs.update(self.special_tokens_map			)
					return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname  ,    **__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Any								=	"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
					__a       :     Optional[int]								=	"""こんにちは、世界。 \nこんばんは、世界。😀"""
					return input_text, output_text
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a							,	__a       :     List[str]								=	self.get_input_output_texts(__UpperCamelCase			)
					__a       :     List[str]								=	tokenizer.encode(__UpperCamelCase  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     str								=	tokenizer.decode(__UpperCamelCase  ,    clean_up_tokenization_spaces=__UpperCamelCase			)
					return text, ids
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass  # TODO add if relevant
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	self.get_tokenizer()

					# Testing tokenization
					__a       :     Dict								=	"""こんにちは、世界。　こんばんは、㔺界。"""
					__a       :     Optional[int]								=	["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
					__a       :     str								=	tokenizer.tokenize(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids without special tokens
					__a       :     Optional[Any]								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
					__a       :     Optional[int]								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)

					# Testing conversion to ids with special tokens
					__a       :     List[str]								=	tokens + [tokenizer.unk_token]
					__a       :     Dict								=	[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
					__a       :     Any								=	tokenizer.convert_tokens_to_ids(__UpperCamelCase			)
					self.assertListEqual(__UpperCamelCase  ,    __UpperCamelCase			)
	@slow
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""			)

					__a       :     Union[str, Any]								=	tokenizer.encode("""ありがとう。"""  ,    add_special_tokens=__UpperCamelCase			)
					__a       :     Dict								=	tokenizer.encode("""どういたしまして。"""  ,    add_special_tokens=__UpperCamelCase			)

					__a       :     Optional[Any]								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase			)
					__a       :     int								=	tokenizer.build_inputs_with_special_tokens(__UpperCamelCase  ,    __UpperCamelCase			)

					assert encoded_sentence == ids_a
					assert encoded_pair == ids_a + ids_a
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					pass