





'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)




@dataclass
class  _A (					__lowercase		):
		lowercase__:   Union[List[PIL.Image.Image], np.ndarray]
		lowercase__:   Optional[List[bool]]
		lowercase__:   Optional[List[bool]]


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
	from .pipeline_if import IFPipeline
	from .pipeline_if_imgaimg import IFImgaImgPipeline
	from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
	from .pipeline_if_inpainting import IFInpaintingPipeline
	from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
	from .pipeline_if_superresolution import IFSuperResolutionPipeline
	from .safety_checker import IFSafetyChecker
	from .watermark import IFWatermarker










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 							snake_case_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



						snake_case__        =      MvpTokenizer
						snake_case__        =      MvpTokenizerFast
						snake_case__        =      True
						snake_case__        =      filter_roberta_detectors



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




											'''simple docstring'''



											super().setUp()
											__a					:						Dict      =					[
											    "l",
											    "o",
											    "w",
											    "e",
											    "r",
											    "s",
											    "t",
											    "i",
											    "d",
											    "n",
											    "\u0120",
											    "\u0120l",
											    "\u0120n",
											    "\u0120lo",
											    "\u0120low",
											    "er",
											    "\u0120lowest",
											    "\u0120newer",
											    "\u0120wider",
											    "<unk>",
											]
											__a					:						Optional[int]      =					dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase   )   )   )   )
											__a					:						Dict      =					["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
											__a					:						Optional[int]      =					{"unk_token": "<unk>"}

											__a					:						Tuple      =					os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["vocab_file"]   )
											__a					:						List[str]      =					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        UpperCAmelCase__					(self:					List[Any] ,					**__UpperCAmelCase:					Dict   )    ->     int:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					str ,					**__UpperCAmelCase:					Tuple   )    ->     Tuple:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					int   )    ->     List[str]:




											'''simple docstring'''



											return "lower newer", "lower newer"



						@cached_property
						def        UpperCAmelCase__					(self:					List[Any]   )    ->     Union[str, Any]:




											'''simple docstring'''



											return MvpTokenizer.from_pretrained("RUCAIBox/mvp"   )



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




											'''simple docstring'''



											return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"   )



						@require_torch
						def        UpperCAmelCase__					(self:					Dict   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					["A long paragraph for summarization.", "Another paragraph for summarization."]
											__a					:						List[Any]      =					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Optional[Any]      =					tokenizer(__UpperCAmelCase ,					max_length=len(__UpperCAmelCase   ) ,					padding=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )

																self.assertEqual((2, 9) ,					batch.input_ids.shape   )
																self.assertEqual((2, 9) ,					batch.attention_mask.shape   )
																__a					:						Optional[int]      =					batch.input_ids.tolist()[0]
																self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase   )
																# Test that special tokens are reset



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




											'''simple docstring'''



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



						@require_torch
						def        UpperCAmelCase__					(self:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						List[Any]      =					[
											    "Summary of the text.",
											    "Another summary.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						int      =					tokenizer(text_target=__UpperCAmelCase ,					max_length=32 ,					padding="max_length" ,					return_tensors="pt"   )
																self.assertEqual(32 ,					targets["input_ids"].shape[1]   )



						@require_torch
						def        UpperCAmelCase__					(self:					int   )    ->     Union[str, Any]:




											'''simple docstring'''



											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						str      =					tokenizer(
																    ["I am a small frog" * 1024, "I am a small frog"] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors="pt"   )
																self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase   )
																self.assertEqual(batch.input_ids.shape ,					(2, 1024)   )



						@require_torch
						def        UpperCAmelCase__					(self:					str   )    ->     Any:




											'''simple docstring'''



											__a					:						Optional[int]      =					["A long paragraph for summarization."]
											__a					:						Tuple      =					[
											    "Summary of the text.",
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																__a					:						Dict      =					tokenizer(__UpperCAmelCase ,					text_target=__UpperCAmelCase ,					return_tensors="pt"   )
																__a					:						str      =					inputs["input_ids"]
																__a					:						Optional[Any]      =					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        UpperCAmelCase__					(self:					Dict   )    ->     str:




											'''simple docstring'''



											pass



						def        UpperCAmelCase__					(self:					Any   )    ->     Optional[Any]:




											'''simple docstring'''



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
																					__a					:						Tuple      =					self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						List[Any]      =					self.tokenizer_class.from_pretrained(__UpperCAmelCase ,					**__UpperCAmelCase   )
																					__a					:						Tuple      =					"A, <mask> AllenNLP sentence."
																					__a					:						Dict      =					tokenizer_r.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )
																					__a					:						List[Any]      =					tokenizer_p.encode_plus(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase   )

																					# 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"]   ) ,					)

																					__a					:						Optional[int]      =					tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]   )
																					__a					:						Union[str, Any]      =					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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )
																					self.assertSequenceEqual(tokens_r["input_ids"] ,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]   )

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





