import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        lowercase_   (					UpperCAmelCase_						,			unittest.TestCase		):
							_lowerCamelCase						=		DebertaTokenizer
							_lowerCamelCase						=		True
							_lowerCamelCase						=		DebertaTokenizerFast



							def 	UpperCamelCase	(						self    ):
													super().setUp()

													# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
													_snake_case						:					Optional[int]    =   [
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''\u0120''',
													    '''\u0120l''',
													    '''\u0120n''',
													    '''\u0120lo''',
													    '''\u0120low''',
													    '''er''',
													    '''\u0120lowest''',
													    '''\u0120newer''',
													    '''\u0120wider''',
													    '''[UNK]''',
													]
													_snake_case						:					List[str]    =   dict(zip(__lowercase				,    range(len(__lowercase    )    )    )    )
													_snake_case						:					Union[str, Any]    =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
													_snake_case						:					Dict    =   {'''unk_token''': '''[UNK]'''}

													_snake_case						:					Any    =   os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["vocab_file"]    )
													_snake_case						:					Union[str, Any]    =   os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["merges_file"]    )
													with open(self.vocab_file				,    "w"				,    encoding="utf-8"    ) as fp:
																			fp.write(json.dumps(__lowercase    ) + "\n"    )
													with open(self.merges_file				,    "w"				,    encoding="utf-8"    ) as fp:
																			fp.write("\n".join(__lowercase    )    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													kwargs.update(self.special_tokens_map    )
													return self.tokenizer_class.from_pretrained(self.tmpdirname				,    **__lowercase    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													_snake_case						:					List[Any]    =   '''lower newer'''
													_snake_case						:					Union[str, Any]    =   '''lower newer'''
													return input_text, output_text



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.get_tokenizer()
													_snake_case						:					Any    =   '''lower newer'''
													_snake_case						:					Optional[int]    =   ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
													_snake_case						:					Optional[int]    =   tokenizer.tokenize(__lowercase    )
													self.assertListEqual(__lowercase				,    __lowercase    )

													_snake_case						:					str    =   tokens + [tokenizer.unk_token]
													_snake_case						:					Dict    =   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase    )				,    __lowercase    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.get_tokenizer()
													_snake_case						:					Union[str, Any]    =   tokenizer("Hello"				,    "World"    )
													_snake_case						:					Optional[Any]    =   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
													self.assertListEqual(tokd["token_type_ids"]				,    __lowercase    )



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.tokenizer_class.from_pretrained("microsoft/deberta-base"    )

													_snake_case						:					Dict    =   tokenizer.encode("sequence builders"				,    add_special_tokens=__lowercase    )
													_snake_case						:					List[Any]    =   tokenizer.encode("multi-sequence build"				,    add_special_tokens=__lowercase    )

													_snake_case						:					Tuple    =   tokenizer.encode(
													    "sequence builders"				,    add_special_tokens=__lowercase				,    add_prefix_space=__lowercase    )
													_snake_case						:					Optional[Any]    =   tokenizer.encode(
													    "sequence builders"				,    "multi-sequence build"				,    add_special_tokens=__lowercase				,    add_prefix_space=__lowercase    )

													_snake_case						:					Optional[int]    =   tokenizer.build_inputs_with_special_tokens(__lowercase    )
													_snake_case						:					Dict    =   tokenizer.build_inputs_with_special_tokens(__lowercase				,    __lowercase    )

													assert encoded_sentence == encoded_text_from_decode
													assert encoded_pair == encoded_pair_from_decode



							@slow
							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   [self.tokenizer_class]
													if self.test_rust_tokenizer:
																			tokenizer_classes.append(self.rust_tokenizer_class    )

													for tokenizer_class in tokenizer_classes:
																			_snake_case						:					str    =   tokenizer_class.from_pretrained("microsoft/deberta-base"    )

																			_snake_case						:					Optional[int]    =   [
																			    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																			    '''ALBERT incorporates two parameter reduction techniques''',
																			    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																			    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																			    ''' vocabulary embedding.''',
																			]

																			_snake_case						:					int    =   tokenizer(__lowercase				,    padding=__lowercase    )
																			_snake_case						:					List[Any]    =   [tokenizer.decode(__lowercase				,    skip_special_tokens=__lowercase    ) for seq in encoding['''input_ids''']]

																			# fmt: off
																			_snake_case						:					Tuple    =   {
																			    '''input_ids''': [
																			        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
																			    ],
																			    '''token_type_ids''': [
																			        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																			    ],
																			    '''attention_mask''': [
																			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																			        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																			    ]
																			}
																			# fmt: on

																			_snake_case						:					Optional[Any]    =   [
																			    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																			    '''ALBERT incorporates two parameter reduction techniques''',
																			    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																			    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																			    ''' vocabulary embedding.''',
																			]

																			self.assertDictEqual(encoding.data				,    __lowercase    )

																			for expected, decoded in zip(__lowercase				,    __lowercase    ):
																									self.assertEqual(__lowercase				,    __lowercase    )
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    ):
													torch.manual_seed(0    )
													_snake_case						:					Tuple    =   TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					List[Any]    =   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"    )

													torch.manual_seed(0    )
													_snake_case						:					Optional[int]    =   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    )
													_snake_case						:					str    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    thresholding=lowercase_				,    dynamic_thresholding_ratio=0.95				,    sample_max_value=1.0				,    prediction_type="epsilon"				,    variance_type="learned_range"				,    )

													torch.manual_seed(0    )
													_snake_case						:					int    =   IFWatermarker()

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



							def 	UpperCamelCase	(						self    ):
													torch.manual_seed(0    )
													_snake_case						:					List[str]    =   TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"    )

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

													torch.manual_seed(0    )
													_snake_case						:					Any    =   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    )
													_snake_case						:					Union[str, Any]    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    thresholding=lowercase_				,    dynamic_thresholding_ratio=0.95				,    sample_max_value=1.0				,    prediction_type="epsilon"				,    variance_type="learned_range"				,    )

													torch.manual_seed(0    )
													_snake_case						:					List[str]    =   DDPMScheduler(
													    num_train_timesteps=1_000				,    beta_schedule="squaredcos_cap_v2"				,    beta_start=0.0_001				,    beta_end=0.02				,    )

													torch.manual_seed(0    )
													_snake_case						:					Dict    =   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    ):
													_snake_case						:					List[Any]    =   self.get_dummy_components()
													_snake_case						:					List[Any]    =   self.pipeline_class(**lowercase_    )
													pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					Union[str, Any]    =   self.get_dummy_inputs(lowercase_    )

													_snake_case						:					Union[str, Any]    =   inputs["prompt"]
													_snake_case						:					Dict    =   inputs["generator"]
													_snake_case						:					Any    =   inputs["num_inference_steps"]
													_snake_case						:					Union[str, Any]    =   inputs["output_type"]

													if "image" in inputs:
																			_snake_case						:					int    =   inputs["image"]
													else:
																			_snake_case						:					Union[str, Any]    =   None

													if "mask_image" in inputs:
																			_snake_case						:					int    =   inputs["mask_image"]
													else:
																			_snake_case						:					List[str]    =   None

													if "original_image" in inputs:
																			_snake_case						:					Tuple    =   inputs["original_image"]
													else:
																			_snake_case						:					Any    =   None

													_snake_case    ,_snake_case						:					Optional[int]    =   pipe.encode_prompt(lowercase_    )

													# inputs with prompt converted to embeddings
													_snake_case						:					Dict    =   {
													    "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:
																			_snake_case						:					int    =   image

													if mask_image is not None:
																			_snake_case						:					int    =   mask_image

													if original_image is not None:
																			_snake_case						:					Any    =   original_image

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

													_snake_case						:					Optional[int]    =   pipe(**lowercase_    )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(lowercase_    )
																			_snake_case						:					Any    =   self.pipeline_class.from_pretrained(lowercase_    )
																			pipe_loaded.to(lowercase_    )
																			pipe_loaded.set_progress_bar_config(disable=lowercase_    )

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

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

													_snake_case						:					int    =   self.get_dummy_inputs(lowercase_    )

													_snake_case						:					Optional[int]    =   inputs["generator"]
													_snake_case						:					List[Any]    =   inputs["num_inference_steps"]
													_snake_case						:					Tuple    =   inputs["output_type"]

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

													if mask_image is not None:
																			_snake_case						:					str    =   mask_image

													if original_image is not None:
																			_snake_case						:					int    =   original_image

													_snake_case						:					Optional[Any]    =   pipe_loaded(**lowercase_    )[0]

													_snake_case						:					Dict    =   np.abs(to_np(lowercase_    ) - to_np(lowercase_    )    ).max()
													self.assertLess(lowercase_				,    1e-4    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_dummy_components()
													_snake_case						:					Any    =   self.pipeline_class(**lowercase_    )
													pipe.to(lowercase_    )
													pipe.set_progress_bar_config(disable=lowercase_    )

													_snake_case						:					int    =   self.get_dummy_inputs(lowercase_    )
													_snake_case						:					List[str]    =   pipe(**lowercase_    )[0]

													with tempfile.TemporaryDirectory() as tmpdir:
																			pipe.save_pretrained(lowercase_    )
																			_snake_case						:					List[Any]    =   self.pipeline_class.from_pretrained(lowercase_    )
																			pipe_loaded.to(lowercase_    )
																			pipe_loaded.set_progress_bar_config(disable=lowercase_    )

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

													_snake_case						:					Optional[Any]    =   self.get_dummy_inputs(lowercase_    )
													_snake_case						:					int    =   pipe_loaded(**lowercase_    )[0]

													_snake_case						:					Tuple    =   np.abs(to_np(lowercase_    ) - to_np(lowercase_    )    ).max()
													self.assertLess(lowercase_				,    1e-4    )