
import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase					: List[str]      =       get_tests_dir("fixtures/spiece.model")


@require_sentencepiece
@require_tokenizers
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       Any	     =				AlbertTokenizer
		__lowercase       :       Optional[int]	     =				AlbertTokenizerFast
		__lowercase       :       Optional[int]	     =				True
		__lowercase       :       Any	     =				True
		__lowercase       :       str	     =				True



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				super().setUp()

				# We have a SentencePiece fixture for testing
				UpperCamelCase		=     AlbertTokenizer(A_       )
				tokenizer.save_pretrained(self.tmpdirname       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'this is a test'
				UpperCamelCase		=     'this is a test'
				return input_text, output_text



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     '<pad>'
				UpperCamelCase		=     0

				self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_       )				,      A_       )
				self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_       )				,      A_       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     list(self.get_tokenizer().get_vocab().keys()       )

				self.assertEqual(vocab_keys[0]				,      '<pad>'       )
				self.assertEqual(vocab_keys[1]				,      '<unk>'       )
				self.assertEqual(vocab_keys[-1]				,      '▁eloquent'       )
				self.assertEqual(len(A_       )				,      30_000       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size				,      30_000       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				if not self.test_rust_tokenizer:
						return

				UpperCamelCase		=     self.get_tokenizer()
				UpperCamelCase		=     self.get_rust_tokenizer()

				UpperCamelCase		=     'I was born in 92000, and this is falsé.'

				UpperCamelCase		=     tokenizer.tokenize(A_       )
				UpperCamelCase		=     rust_tokenizer.tokenize(A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     tokenizer.encode(A_				,      add_special_tokens=A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_				,      add_special_tokens=A_       )
				self.assertListEqual(A_				,      A_       )

				UpperCamelCase		=     self.get_rust_tokenizer()
				UpperCamelCase		=     tokenizer.encode(A_       )
				UpperCamelCase		=     rust_tokenizer.encode(A_       )
				self.assertListEqual(A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     AlbertTokenizer(A_				,      keep_accents=A_       )

				UpperCamelCase		=     tokenizer.tokenize('This is a test'       )
				self.assertListEqual(A_				,      ['▁this', '▁is', '▁a', '▁test']       )

				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )				,      [48, 25, 21, 1_289]       )

				UpperCamelCase		=     tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
				self.assertListEqual(
				    A_				,      ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']       )
				UpperCamelCase		=     tokenizer.convert_tokens_to_ids(A_       )
				self.assertListEqual(A_				,      [31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]       )

				UpperCamelCase		=     tokenizer.convert_ids_to_tokens(A_       )
				self.assertListEqual(
				    A_				,      ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']				,      )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     AlbertTokenizer(A_       )

				UpperCamelCase		=     tokenizer.encode('sequence builders'       )
				UpperCamelCase		=     tokenizer.encode('multi-sequence build'       )

				UpperCamelCase		=     tokenizer.build_inputs_with_special_tokens(A_       )
				UpperCamelCase		=     tokenizer.build_inputs_with_special_tokens(A_				,      A_       )

				assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
				assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
				    tokenizer.sep_token_id
				]



		@slow
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				# fmt: off
				UpperCamelCase		=     {'attention_mask': [[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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=A_				,      model_name='albert-base-v2'				,      revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'				,      )


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
