






import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    int       						=  RobertaTokenizer
		_snake_case    :    Any       						=  RobertaTokenizerFast
		_snake_case    :    Optional[int]       						=  True
		_snake_case    :    List[Any]       						=  {'''cls_token''': '''<s>'''}



		def 					__UpperCAmelCase			(							self   )	->							int:
							super().setUp()

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

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



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							str:
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							Optional[int]:
							kwargs.update(self.special_tokens_map   )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Any:
							UpperCAmelCase_    :      List[Any]            =						'lower newer'
							UpperCAmelCase_    :      Optional[int]            =						'lower newer'
							return input_text, output_text



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      Dict            =						self.tokenizer_class(self.vocab_file				,		self.merges_file				,		**self.special_tokens_map   )
							UpperCAmelCase_    :      Optional[Any]            =						'lower newer'
							UpperCAmelCase_    :      Optional[Any]            =						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							UpperCAmelCase_    :      Optional[int]            =						tokenizer.tokenize(_UpperCamelCase   )  # , add_prefix_space=True)
							self.assertListEqual(_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Tuple            =						tokens + [tokenizer.unk_token]
							UpperCAmelCase_    :      Optional[Any]            =						[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase   )				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Dict            =						self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'				,		add_special_tokens=_UpperCamelCase   )				,		[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]   )
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'				,		add_special_tokens=_UpperCamelCase   )				,		[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]				,		)



		@slow
		def 					__UpperCAmelCase			(							self   )	->							Any:
							UpperCAmelCase_    :      List[Any]            =						self.tokenizer_class.from_pretrained('roberta-base'   )

							UpperCAmelCase_    :      str            =						tokenizer.encode('sequence builders'				,		add_special_tokens=_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						tokenizer.encode('multi-sequence build'				,		add_special_tokens=_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[int]            =						tokenizer.encode(
							    'sequence builders'				,		add_special_tokens=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase   )
							UpperCAmelCase_    :      Tuple            =						tokenizer.encode(
							    'sequence builders'				,		'multi-sequence build'				,		add_special_tokens=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase   )

							UpperCAmelCase_    :      int            =						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						tokenizer.build_inputs_with_special_tokens(_UpperCamelCase				,		_UpperCamelCase   )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							UpperCAmelCase_    :      Optional[Any]            =						self.get_tokenizer()

							UpperCAmelCase_    :      Any            =						'Encode this sequence.'
							UpperCAmelCase_    :      int            =						tokenizer.byte_encoder[' '.encode('utf-8'   )[0]]

							# Testing encoder arguments
							UpperCAmelCase_    :      Optional[Any]            =						tokenizer.encode(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase   )
							UpperCAmelCase_    :      int            =						tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertNotEqual(_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      str            =						tokenizer.encode(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

							tokenizer.add_special_tokens({'bos_token': '<s>'}   )
							UpperCAmelCase_    :      List[Any]            =						tokenizer.encode(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
							UpperCAmelCase_    :      Union[str, Any]            =						tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
							self.assertNotEqual(_UpperCamelCase				,		_UpperCamelCase   )

							# Testing spaces after special tokens
							UpperCAmelCase_    :      Any            =						'<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(_UpperCamelCase				,		lstrip=_UpperCamelCase				,		rstrip=_UpperCamelCase   )}   )  # mask token has a left space
							UpperCAmelCase_    :      Union[str, Any]            =						tokenizer.convert_tokens_to_ids(_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						'Encode <mask> sequence'
							UpperCAmelCase_    :      Union[str, Any]            =						'Encode <mask>sequence'

							UpperCAmelCase_    :      int            =						tokenizer.encode(_UpperCamelCase   )
							UpperCAmelCase_    :      Any            =						encoded.index(_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						tokenizer.encode(_UpperCamelCase   )
							UpperCAmelCase_    :      List[Any]            =						encoded.index(_UpperCamelCase   )
							UpperCAmelCase_    :      Dict            =						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertNotEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							pass



		def 					__UpperCAmelCase			(							self   )	->							str:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
																	UpperCAmelCase_    :      int            =						self.rust_tokenizer_class.from_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )
																	UpperCAmelCase_    :      List[Any]            =						self.tokenizer_class.from_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )
																	UpperCAmelCase_    :      Any            =						'A, <mask> AllenNLP sentence.'
																	UpperCAmelCase_    :      Optional[int]            =						tokenizer_r.encode_plus(_UpperCamelCase				,		add_special_tokens=_UpperCamelCase				,		return_token_type_ids=_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[int]            =						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']   )				,		)

																	UpperCAmelCase_    :      List[str]            =						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
																	UpperCAmelCase_    :      Optional[int]            =						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_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )
																	self.assertSequenceEqual(tokens_r['input_ids']				,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]   )

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



		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							for trim_offsets, add_prefix_space in itertools.product([True, False]				,		repeat=2   ):
												UpperCAmelCase_    :      Optional[Any]            =						self.rust_tokenizer_class.from_pretrained(
												    self.tmpdirname				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )

												UpperCAmelCase_    :      Optional[Any]            =						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
												UpperCAmelCase_    :      str            =						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

												self.assertEqual(pre_tokenizer_state['add_prefix_space']				,		_UpperCamelCase   )

												self.assertEqual(post_processor_state['add_prefix_space']				,		_UpperCamelCase   )
												self.assertEqual(post_processor_state['trim_offsets']				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
																	UpperCAmelCase_    :      Optional[int]            =						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
																	UpperCAmelCase_    :      Any            =						f"{text_of_1_token} {text_of_1_token}"

																	UpperCAmelCase_    :      Dict            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      int            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(len(_UpperCamelCase   ) + 1, len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      List[Any]            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      Any            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(len(_UpperCamelCase   ) + 1, len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      Tuple            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      str            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(len(_UpperCamelCase   ), len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      int            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[int]            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(len(_UpperCamelCase   ), len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      List[Any]            =						f" {text}"

																	# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																	#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																	# )
																	# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																	# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																	# self.assertEqual(
																	#     encoding.offset_mapping[1],
																	#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																	# )

																	UpperCAmelCase_    :      Optional[Any]            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      Optional[Any]            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(1, 1 + len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(1 + len(_UpperCamelCase   ) + 1, 1 + len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      Tuple            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      Dict            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, 1 + len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(1 + len(_UpperCamelCase   ), 1 + len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)

																	UpperCAmelCase_    :      Any            =						self.rust_tokenizer_class.from_pretrained(
																	    _UpperCamelCase				,		use_fast=_UpperCamelCase				,		add_prefix_space=_UpperCamelCase				,		trim_offsets=_UpperCamelCase   )
																	UpperCAmelCase_    :      Dict            =						tokenizer_r(_UpperCamelCase				,		return_offsets_mapping=_UpperCamelCase				,		add_special_tokens=_UpperCamelCase   )
																	self.assertEqual(encoding.offset_mapping[0]				,		(0, 1 + len(_UpperCamelCase   ))   )
																	self.assertEqual(
																	    encoding.offset_mapping[1]				,		(1 + len(_UpperCamelCase   ), 1 + len(_UpperCamelCase   ) + 1 + len(_UpperCamelCase   ))				,		)







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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


