




'''simple docstring'''


import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class    __lowercase						(  __snake_case       ,							unittest.TestCase ):
   _a							=      GPTSanJapaneseTokenizer
   _a							=      False
   _a							=      {"""do_clean_text""": False, """add_prefix_space""": False}







   def 		UpperCamelCase__			(	self      )				->    Any:
     super().setUp()

     # fmt: off
     __a			=					["""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>""", """<|bagoftoken|>""", """<|endoftext|>"""]
     # fmt: on
     __a			=					{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
     __a			=					{"""unk_token""": """<unk>"""}

     __a			=					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['vocab_file']      )
     __a			=					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(_lowercase      )      )







   def 		UpperCamelCase__			(	self      ,							**UpperCamelCase      )				->    Tuple:
     kwargs.update(self.special_tokens_map      )
     return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname      ,							**_lowercase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Dict:
     __a			=					"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
     __a			=					"""こんにちは、世界。 \nこんばんは、世界。😀"""
     return input_text, output_text







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     __a			=					self.get_input_output_texts(_lowercase      )
     __a			=					tokenizer.encode(_lowercase      ,							add_special_tokens=_lowercase      )
     __a			=					tokenizer.decode(_lowercase      ,							clean_up_tokenization_spaces=_lowercase      )
     return text, ids







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     pass  # TODO add if relevant







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     pass  # TODO add if relevant







   def 		UpperCamelCase__			(	self      )				->    str:
     pass  # TODO add if relevant







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					self.get_tokenizer()

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

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

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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_tokenizer()

     # Testing tokenization
     __a			=					"""こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
     __a			=					"""こんにちは、、、、世界。こんばんは、、、、世界。"""
     __a			=					tokenizer.encode(_lowercase      )
     __a			=					tokenizer.decode(_lowercase      )
     self.assertEqual(_lowercase      ,							_lowercase      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    Any:
     __a			=					self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'      )

     # Testing tokenization
     __a			=					"""こんにちは、世界。"""
     __a			=					"""こんばんは、㔺界。😀"""
     __a			=					"""こんにちは、世界。こんばんは、世界。😀"""
     __a			=					tokenizer.encode(prefix_text + input_text      )
     __a			=					tokenizer.encode(''      ,							prefix_text=prefix_text + input_text      )
     __a			=					tokenizer.encode(_lowercase      ,							prefix_text=_lowercase      )
     __a			=					tokenizer.decode(_lowercase      )
     __a			=					tokenizer.decode(_lowercase      )
     __a			=					tokenizer.decode(_lowercase      )
     self.assertEqual(_lowercase      ,							_lowercase      )
     self.assertEqual(_lowercase      ,							_lowercase      )
     self.assertEqual(_lowercase      ,							_lowercase      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'      )

     # Testing tokenization
     __a			=					"""こんにちは、世界。"""
     __a			=					"""こんばんは、㔺界。😀"""

     __a			=					len(tokenizer.encode(_lowercase      )      ) - 2
     __a			=					len(tokenizer.encode(_lowercase      )      ) - 2

     __a			=					[1] + [0] * (len_prefix + len_text + 1)
     __a			=					[1] * (len_prefix + len_text + 1) + [0]
     __a			=					[1] + [1] * (len_prefix) + [0] * (len_text + 1)

     __a			=					tokenizer(prefix_text + input_text      ).token_type_ids
     __a			=					tokenizer(''      ,							prefix_text=prefix_text + input_text      ).token_type_ids
     __a			=					tokenizer(_lowercase      ,							prefix_text=_lowercase      ).token_type_ids
     self.assertListEqual(_lowercase      ,							_lowercase      )
     self.assertListEqual(_lowercase      ,							_lowercase      )
     self.assertListEqual(_lowercase      ,							_lowercase      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'      )

     __a			=					tokenizer.encode('あンいワ'      )
     __a			=					tokenizer.encode(''      ,							prefix_text='あンいワ'      )
     __a			=					tokenizer.encode('いワ'      ,							prefix_text='あン'      )

     self.assertEqual(tokenizer.decode(_lowercase      )      ,							tokenizer.decode(_lowercase      )      )
     self.assertEqual(tokenizer.decode(_lowercase      )      ,							tokenizer.decode(_lowercase      )      )
     self.assertNotEqual(_lowercase      ,							_lowercase      )
     self.assertNotEqual(_lowercase      ,							_lowercase      )
     self.assertEqual(x_token_a[1]      ,							x_token_a[-1]      )  # SEG token
     self.assertEqual(x_token_a[1]      ,							x_token_a[3]      )  # SEG token







   @slow
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'      )

     __a			=					[["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
     __a			=					tokenizer(_lowercase      ,							padding=_lowercase      )
     __a			=					tokenizer.batch_encode_plus(_lowercase      ,							padding=_lowercase      )

     # fmt: off
     __a			=					[[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
     __a			=					[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
     __a			=					[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
     # fmt: on
     self.assertListEqual(x_token.input_ids      ,							_lowercase      )
     self.assertListEqual(x_token.token_type_ids      ,							_lowercase      )
     self.assertListEqual(x_token.attention_mask      ,							_lowercase      )
     self.assertListEqual(x_token_a.input_ids      ,							_lowercase      )
     self.assertListEqual(x_token_a.token_type_ids      ,							_lowercase      )
     self.assertListEqual(x_token_a.attention_mask      ,							_lowercase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     pass







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     pass









'''simple docstring'''


import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class    __lowercase						:







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=1      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=3      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     torch.manual_seed(0      )
     __a			=					TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'      )

     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         sample_size=32      ,							layers_per_block=[1, 2]      ,							block_out_channels=[32, 64]      ,							down_block_types=[
             'ResnetDownsampleBlock2D',
             'SimpleCrossAttnDownBlock2D',
         ]      ,							mid_block_type='UNetMidBlock2DSimpleCrossAttn'      ,							up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']      ,							in_channels=6      ,							out_channels=6      ,							cross_attention_dim=32      ,							encoder_hid_dim=32      ,							attention_head_dim=8      ,							addition_embed_type='text'      ,							addition_embed_type_num_heads=2      ,							cross_attention_norm='group_norm'      ,							resnet_time_scale_shift='scale_shift'      ,							act_fn='gelu'      ,							class_embed_type='timestep'      ,							mid_block_scale_factor=1.414      ,							time_embedding_act_fn='gelu'      ,							time_embedding_dim=32      ,							)
     unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							thresholding=UpperCamelCase      ,							dynamic_thresholding_ratio=0.95      ,							sample_max_value=1.0      ,							prediction_type='epsilon'      ,							variance_type='learned_range'      ,							)

     torch.manual_seed(0      )
     __a			=					DDPMScheduler(
         num_train_timesteps=1000      ,							beta_schedule='squaredcos_cap_v2'      ,							beta_start=0.0_001      ,							beta_end=0.02      ,							)

     torch.manual_seed(0      )
     __a			=					IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['prompt']
     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     if "image" in inputs:
       __a			=					inputs['image']
     else:
       __a			=					None

     if "mask_image" in inputs:
       __a			=					inputs['mask_image']
     else:
       __a			=					None

     if "original_image" in inputs:
       __a			=					inputs['original_image']
     else:
       __a			=					None

     __a	,       __a			=					pipe.encode_prompt(UpperCamelCase      )

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
       setattr(UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )

     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     for optional_component in pipe._optional_components:
       self.assertTrue(
           getattr(UpperCamelCase      ,							UpperCamelCase      ) is None      ,							f"`{optional_component}` did not stay set to None after loading."      ,							)

     __a			=					self.get_dummy_inputs(UpperCamelCase      )

     __a			=					inputs['generator']
     __a			=					inputs['num_inference_steps']
     __a			=					inputs['output_type']

     # inputs with prompt converted to embeddings
     __a			=					{
         'prompt_embeds': prompt_embeds,
         'negative_prompt_embeds': negative_prompt_embeds,
         'generator': generator,
         'num_inference_steps': num_inference_steps,
         'output_type': output_type,
     }

     if image is not None:
       __a			=					image

     if mask_image is not None:
       __a			=					mask_image

     if original_image is not None:
       __a			=					original_image

     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
       pipe.save_pretrained(UpperCamelCase      )
       __a			=					self.pipeline_class.from_pretrained(UpperCamelCase      )
       pipe_loaded.to(UpperCamelCase      )
       pipe_loaded.set_progress_bar_config(disable=UpperCamelCase      )

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe_loaded(**UpperCamelCase      )[0]

     __a			=					np.abs(to_np(UpperCamelCase      ) - to_np(UpperCamelCase      )      ).max()
     self.assertLess(UpperCamelCase      ,							1e-4      )



