
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    __A:

					def  lowercase__   (						self       :  int ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

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

												torch.manual_seed(0 )
												lowerCamelCase_					=       UNetaDConditionModel(
												    sample_size=3_2 ,       layers_per_block=1 ,       block_out_channels=[3_2, 6_4] ,       down_block_types=[
												        """ResnetDownsampleBlock2D""",
												        """SimpleCrossAttnDownBlock2D""",
												    ] ,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,       in_channels=3 ,       out_channels=6 ,       cross_attention_dim=3_2 ,       encoder_hid_dim=3_2 ,       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 )
												lowerCamelCase_					=       DDPMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_schedule="""squaredcos_cap_v2""" ,       beta_start=0.0001 ,       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 )
												lowerCamelCase_					=       IFWatermarker()

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

					def  lowercase__   (						self       :  Any ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

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

												torch.manual_seed(0 )
												lowerCamelCase_					=       UNetaDConditionModel(
												    sample_size=3_2 ,       layers_per_block=[1, 2] ,       block_out_channels=[3_2, 6_4] ,       down_block_types=[
												        """ResnetDownsampleBlock2D""",
												        """SimpleCrossAttnDownBlock2D""",
												    ] ,       mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" ,       up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] ,       in_channels=6 ,       out_channels=6 ,       cross_attention_dim=3_2 ,       encoder_hid_dim=3_2 ,       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=3_2 ,       )
												unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

												torch.manual_seed(0 )
												lowerCamelCase_					=       DDPMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_schedule="""squaredcos_cap_v2""" ,       beta_start=0.0001 ,       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 )
												lowerCamelCase_					=       DDPMScheduler(
												    num_train_timesteps=1_0_0_0 ,       beta_schedule="""squaredcos_cap_v2""" ,       beta_start=0.0001 ,       beta_end=0.02 ,       )

												torch.manual_seed(0 )
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       self.pipeline_class(**__UpperCamelCase )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )

												lowerCamelCase_					=       inputs["""prompt"""]
												lowerCamelCase_					=       inputs["""generator"""]
												lowerCamelCase_					=       inputs["""num_inference_steps"""]
												lowerCamelCase_					=       inputs["""output_type"""]

												if "image" in inputs:
																			lowerCamelCase_					=       inputs["""image"""]
												else:
																			lowerCamelCase_					=       None

												if "mask_image" in inputs:
																			lowerCamelCase_					=       inputs["""mask_image"""]
												else:
																			lowerCamelCase_					=       None

												if "original_image" in inputs:
																			lowerCamelCase_					=       inputs["""original_image"""]
												else:
																			lowerCamelCase_					=       None

												lowerCamelCase_	, lowerCamelCase_					=       pipe.encode_prompt(__UpperCamelCase )

												# inputs with prompt converted to embeddings
												lowerCamelCase_					=       {
												    """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:
																			lowerCamelCase_					=       image

												if mask_image is not None:
																			lowerCamelCase_					=       mask_image

												if original_image is not None:
																			lowerCamelCase_					=       original_image

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

												lowerCamelCase_					=       pipe(**__UpperCamelCase )[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(__UpperCamelCase )
																			lowerCamelCase_					=       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.''' ,       )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )

												lowerCamelCase_					=       inputs["""generator"""]
												lowerCamelCase_					=       inputs["""num_inference_steps"""]
												lowerCamelCase_					=       inputs["""output_type"""]

												# inputs with prompt converted to embeddings
												lowerCamelCase_					=       {
												    """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:
																			lowerCamelCase_					=       image

												if mask_image is not None:
																			lowerCamelCase_					=       mask_image

												if original_image is not None:
																			lowerCamelCase_					=       original_image

												lowerCamelCase_					=       pipe_loaded(**__UpperCamelCase )[0]

												lowerCamelCase_					=       np.abs(to_np(__UpperCamelCase ) - to_np(__UpperCamelCase ) ).max()
												self.assertLess(__UpperCamelCase ,       1E-4 )



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       self.pipeline_class(**__UpperCamelCase )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       pipe(**__UpperCamelCase )[0]

												with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(__UpperCamelCase )
																			lowerCamelCase_					=       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

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       pipe_loaded(**__UpperCamelCase )[0]

												lowerCamelCase_					=       np.abs(to_np(__UpperCamelCase ) - to_np(__UpperCamelCase ) ).max()
												self.assertLess(__UpperCamelCase ,       1E-4 )










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        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     GPTSanJapaneseTokenizer
				A_   :     Optional[int]								=     False
				A_   :     List[str]								=     {'do_clean_text': False, 'add_prefix_space': False}


				def __UpperCamelCase						(							self      :  Tuple       )		->     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(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Any						, **__UpperCamelCase      :  Any       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str						, __UpperCamelCase      :  Dict       )		->     List[str]:
											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      :  str						, __UpperCamelCase      :  Optional[Any]       )		->     List[str]:
											A						,   A      						=    self.get_input_output_texts(__UpperCamelCase       )
											A      						=    tokenizer.encode(__UpperCamelCase						, add_special_tokens=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase						, clean_up_tokenization_spaces=__UpperCamelCase       )
											return text, ids


				def __UpperCamelCase						(							self      :  Any       )		->     int:
											pass  # TODO add if relevant


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


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


				def __UpperCamelCase						(							self      :  List[str]       )		->     int:
											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(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# 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(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											# 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(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											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(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     str:
											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(__UpperCamelCase						, prefix_text=__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											A      						=    tokenizer.decode(__UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[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      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 2
											A      						=    len(tokenizer.encode(__UpperCamelCase       )       ) - 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(__UpperCamelCase						, prefix_text=__UpperCamelCase       ).token_type_ids
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     str:
											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(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertEqual(tokenizer.decode(__UpperCamelCase       )						, tokenizer.decode(__UpperCamelCase       )       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											self.assertNotEqual(__UpperCamelCase						, __UpperCamelCase       )
											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      :  Any       )		->     List[Any]:
											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(__UpperCamelCase						, padding=__UpperCamelCase       )
											A      						=    tokenizer.batch_encode_plus(__UpperCamelCase						, padding=__UpperCamelCase       )

											# fmt: off
											A      						=    [[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
											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						, __UpperCamelCase       )
											self.assertListEqual(x_token.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token.attention_mask						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.input_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.token_type_ids						, __UpperCamelCase       )
											self.assertListEqual(x_token_a.attention_mask						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple       )		->     str:
											# Intentionally convert some words to accommodate character fluctuations unique to Japanese
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     int:
											# tokenizer has no padding token
											pass