



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class     __lowerCAmelCase					(    A       ,		unittest.TestCase		):
			UpperCamelCase           =  MvpTokenizer
			UpperCamelCase           =  MvpTokenizerFast
			UpperCamelCase           =  True
			UpperCamelCase           =  filter_roberta_detectors



			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						super().setUp()
						_UpperCAmelCase	 =     [
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						]
						_UpperCAmelCase	 =     dict(zip(A				,	range(len(A))))
						_UpperCAmelCase	 =     ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						_UpperCAmelCase	 =     {'unk_token': '<unk>'}

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



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	**A     :						Optional[int])     ->					Dict:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return self.tokenizer_class.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						List[Any]				,	**A     :						Union[str, Any])     ->					int:



						"""simple docstring"""






						kwargs.update(self.special_tokens_map)
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,	**A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						str)     ->					Dict:



						"""simple docstring"""






						return "lower newer", "lower newer"



			@cached_property
			def 					_lowerCamelCase (		self     :						Any)     ->					str:



						"""simple docstring"""






						return MvpTokenizer.from_pretrained('RUCAIBox/mvp')



			@cached_property
			def 					_lowerCamelCase (		self     :						Any)     ->					List[Any]:



						"""simple docstring"""






						return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')



			@require_torch
			def 					_lowerCamelCase (		self     :						List[Any])     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						_UpperCAmelCase	 =     [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									_UpperCAmelCase	 =     tokenizer(A				,	max_length=len(A)				,	padding=A				,	return_tensors='pt')
									self.assertIsInstance(A				,	A)

									self.assertEqual((2, 9)				,	batch.input_ids.shape)
									self.assertEqual((2, 9)				,	batch.attention_mask.shape)
									_UpperCAmelCase	 =     batch.input_ids.tolist()[0]
									self.assertListEqual(A				,	A)
									# Test that special tokens are reset



			@require_torch
			def 					_lowerCamelCase (		self     :						Dict)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     ['A long paragraph for summarization.', 'Another paragraph for summarization.']
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									_UpperCAmelCase	 =     tokenizer(A				,	padding=A				,	return_tensors='pt')
									# check if input_ids are returned and no labels
									self.assertIn('input_ids'				,	A)
									self.assertIn('attention_mask'				,	A)
									self.assertNotIn('labels'				,	A)
									self.assertNotIn('decoder_attention_mask'				,	A)



			@require_torch
			def 					_lowerCamelCase (		self     :						List[Any])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [
						    'Summary of the text.',
						    'Another summary.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									_UpperCAmelCase	 =     tokenizer(text_target=A				,	max_length=32				,	padding='max_length'				,	return_tensors='pt')
									self.assertEqual(32				,	targets['input_ids'].shape[1])



			@require_torch
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									_UpperCAmelCase	 =     tokenizer(
									    ['I am a small frog' * 10_24, 'I am a small frog']				,	padding=A				,	truncation=A				,	return_tensors='pt')
									self.assertIsInstance(A				,	A)
									self.assertEqual(batch.input_ids.shape				,	(2, 10_24))



			@require_torch
			def 					_lowerCamelCase (		self     :						List[str])     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     ['A long paragraph for summarization.']
						_UpperCAmelCase	 =     [
						    'Summary of the text.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									_UpperCAmelCase	 =     tokenizer(A				,	text_target=A				,	return_tensors='pt')
									_UpperCAmelCase	 =     inputs['input_ids']
									_UpperCAmelCase	 =     inputs['labels']
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						List[Any])     ->					Tuple:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase	 =     self.rust_tokenizer_class.from_pretrained(A				,	**A)
												_UpperCAmelCase	 =     self.tokenizer_class.from_pretrained(A				,	**A)
												_UpperCAmelCase	 =     'A, <mask> AllenNLP sentence.'
												_UpperCAmelCase	 =     tokenizer_r.encode_plus(A				,	add_special_tokens=A				,	return_token_type_ids=A)
												_UpperCAmelCase	 =     tokenizer_p.encode_plus(A				,	add_special_tokens=A				,	return_token_type_ids=A)

												# token_type_ids should put 0 everywhere
												self.assertEqual(sum(tokens_r['token_type_ids'])				,	sum(tokens_p['token_type_ids']))

												# attention_mask should put 1 everywhere, so sum over length should be 1
												self.assertEqual(
												    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])				,	sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])				,	)

												_UpperCAmelCase	 =     tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
												_UpperCAmelCase	 =     tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

												# Rust correctly handles the space before the mask while python doesnt
												self.assertSequenceEqual(tokens_p['input_ids']				,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
												self.assertSequenceEqual(tokens_r['input_ids']				,	[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

												self.assertSequenceEqual(
												    A				,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
												self.assertSequenceEqual(
												    A				,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])





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     __lowerCAmelCase					(    A       ,		A       ,		unittest.TestCase		):
			UpperCamelCase           =  IFInpaintingPipeline
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
			UpperCamelCase           =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCamelCase           =  PipelineTesterMixin.required_optional_params - {'''latents'''}



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""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 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1E-1)



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



			def 					_lowerCamelCase (		self     :						str)     ->					List[str]:



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1E-2				,	)
