


"""simple docstring"""


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   _UpperCamelCase  (   __lowercase       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =DebertaTokenizer
							__UpperCAmelCase						:    Union[str, Any]				      =True
							__UpperCAmelCase						:    List[Any]				      =DebertaTokenizerFast

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

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										__lowerCAmelCase				    =			[
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "[UNK]",
										]
										__lowerCAmelCase				    =			dict(zip(UpperCAmelCase__    ,   range(len(UpperCAmelCase__	)	)	)	)
										__lowerCAmelCase				    =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										__lowerCAmelCase				    =			{"unk_token": "[UNK]"}

										__lowerCAmelCase				    =			os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]	)
										__lowerCAmelCase				    =			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(UpperCAmelCase__	) + "\n"	)
										with open(self.merges_file    ,   "w"    ,   encoding="utf-8"	) as fp:
													fp.write("\n".join(UpperCAmelCase__	)	)

							def 			snake_case				(     self    ,   **__a	):
										kwargs.update(self.special_tokens_map	)
										return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **UpperCAmelCase__	)

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			"lower newer"
										__lowerCAmelCase				    =			"lower newer"
										return input_text, output_text

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			"lower newer"
										__lowerCAmelCase				    =			["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										__lowerCAmelCase				    =			tokenizer.tokenize(UpperCAmelCase__	)
										self.assertListEqual(UpperCAmelCase__    ,   UpperCAmelCase__	)

										__lowerCAmelCase				    =			tokens + [tokenizer.unk_token]
										__lowerCAmelCase				    =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__	)    ,   UpperCAmelCase__	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.get_tokenizer()
										__lowerCAmelCase				    =			tokenizer("Hello"    ,   "World"	)
										__lowerCAmelCase				    =			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd["token_type_ids"]    ,   UpperCAmelCase__	)

							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.tokenizer_class.from_pretrained("microsoft/deberta-base"	)

										__lowerCAmelCase				    =			tokenizer.encode("sequence builders"    ,   add_special_tokens=UpperCAmelCase__	)
										__lowerCAmelCase				    =			tokenizer.encode("multi-sequence build"    ,   add_special_tokens=UpperCAmelCase__	)

										__lowerCAmelCase				    =			tokenizer.encode(
										    "sequence builders"    ,   add_special_tokens=UpperCAmelCase__    ,   add_prefix_space=UpperCAmelCase__	)
										__lowerCAmelCase				    =			tokenizer.encode(
										    "sequence builders"    ,   "multi-sequence build"    ,   add_special_tokens=UpperCAmelCase__    ,   add_prefix_space=UpperCAmelCase__	)

										__lowerCAmelCase				    =			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__	)
										__lowerCAmelCase				    =			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__    ,   UpperCAmelCase__	)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode



							@slow
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			[self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class	)

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

													__lowerCAmelCase				    =			[
													    "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.",
													]

													__lowerCAmelCase				    =			tokenizer(UpperCAmelCase__    ,   padding=UpperCAmelCase__	)
													__lowerCAmelCase				    =			[tokenizer.decode(UpperCAmelCase__    ,   skip_special_tokens=UpperCAmelCase__	) for seq in encoding["input_ids"]]

													# fmt: off
													__lowerCAmelCase				    =			{
													    "input_ids": [
													        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

													__lowerCAmelCase				    =			[
													    "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    ,   UpperCAmelCase__	)

													for expected, decoded in zip(UpperCAmelCase__    ,   UpperCAmelCase__	):
																self.assertEqual(UpperCAmelCase__    ,   UpperCAmelCase__	)








'''simple docstring'''




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2





