





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   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					RobertaTokenizer
							lowerCamelCase_           =					RobertaTokenizerFast
							lowerCamelCase_           =					True
							lowerCamelCase_           =					{"cls_token": "<s>"}


							def      _snake_case  (							self    :List[str]		)   ->   Dict:



									"""simple docstring"""



									super().setUp()

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

									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :List[str]							,				**__A    :List[Any]		)   ->   List[str]:



									"""simple docstring"""



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


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



									"""simple docstring"""



									kwargs.update(self.special_tokens_map		)
									return RobertaTokenizerFast.from_pretrained(self.tmpdirname							,				**__A		)


							def      _snake_case  (							self    :str							,				__A    :Dict		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """lower newer"""
									SCREAMING_SNAKE_CASE__			=      """lower newer"""
									return input_text, output_text


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer_class(self.vocab_file							,				self.merges_file							,				**self.special_tokens_map		)
									SCREAMING_SNAKE_CASE__			=      """lower newer"""
									SCREAMING_SNAKE_CASE__			=      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
									SCREAMING_SNAKE_CASE__			=      tokenizer.tokenize(__A		)  # , add_prefix_space=True)
									self.assertListEqual(__A							,				__A		)

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


							def      _snake_case  (							self    :Optional[Any]		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									self.assertListEqual(tokenizer.encode("""Hello world!"""							,				add_special_tokens=__A		)							,				[0, 3_1414, 232, 328, 2]		)
									self.assertListEqual(
									    tokenizer.encode("""Hello world! cécé herlolip 418"""							,				add_special_tokens=__A		)							,				[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]							,				)


							@slow
							def      _snake_case  (							self    :List[Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.tokenizer_class.from_pretrained("""roberta-base"""		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""sequence builders"""							,				add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode("""multi-sequence build"""							,				add_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(
									    """sequence builders"""							,				add_special_tokens=__A							,				add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(
									    """sequence builders"""							,				"""multi-sequence build"""							,				add_special_tokens=__A							,				add_prefix_space=__A		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.build_inputs_with_special_tokens(__A							,				__A		)

									assert encoded_sentence == encoded_text_from_decode
									assert encoded_pair == encoded_pair_from_decode


							def      _snake_case  (							self    :Any		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									SCREAMING_SNAKE_CASE__			=      """Encode this sequence."""
									SCREAMING_SNAKE_CASE__			=      tokenizer.byte_encoder[""" """.encode("""utf-8"""		)[0]]

									# Testing encoder arguments
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A							,				add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
									self.assertNotEqual(__A							,				__A		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A							,				add_prefix_space=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
									self.assertEqual(__A							,				__A		)

									tokenizer.add_special_tokens({"""bos_token""": """<s>"""}		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A							,				add_special_tokens=__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
									self.assertNotEqual(__A							,				__A		)

									# Testing spaces after special tokens
									SCREAMING_SNAKE_CASE__			=      """<mask>"""
									tokenizer.add_special_tokens(
									    {"""mask_token""": AddedToken(__A							,				lstrip=__A							,				rstrip=__A		)}		)  # mask token has a left space
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_tokens_to_ids(__A		)

									SCREAMING_SNAKE_CASE__			=      """Encode <mask> sequence"""
									SCREAMING_SNAKE_CASE__			=      """Encode <mask>sequence"""

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A		)
									SCREAMING_SNAKE_CASE__			=      encoded.index(__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
									self.assertEqual(__A							,				__A		)

									SCREAMING_SNAKE_CASE__			=      tokenizer.encode(__A		)
									SCREAMING_SNAKE_CASE__			=      encoded.index(__A		)
									SCREAMING_SNAKE_CASE__			=      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
									self.assertNotEqual(__A							,				__A		)


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



									"""simple docstring"""



									pass


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



									"""simple docstring"""



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

													SCREAMING_SNAKE_CASE__			=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
													SCREAMING_SNAKE_CASE__			=      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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r["""input_ids"""]							,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]		)

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


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



									"""simple docstring"""



									for trim_offsets, add_prefix_space in itertools.product([True, False]							,				repeat=2		):
											SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)

											SCREAMING_SNAKE_CASE__			=      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
											SCREAMING_SNAKE_CASE__			=      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

											self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]							,				__A		)

											self.assertEqual(post_processor_state["""add_prefix_space"""]							,				__A		)
											self.assertEqual(post_processor_state["""trim_offsets"""]							,				__A		)


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



									"""simple docstring"""



									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
													SCREAMING_SNAKE_CASE__			=      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
													SCREAMING_SNAKE_CASE__			=      f'''{text_of_1_token} {text_of_1_token}'''

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(len(__A		) + 1, len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(len(__A		) + 1, len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(len(__A		), len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(len(__A		), len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      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)),
													# )

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(1, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(1 + len(__A		) + 1, 1 + len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))							,				)

													SCREAMING_SNAKE_CASE__			=      self.rust_tokenizer_class.from_pretrained(
													    __A							,				use_fast=__A							,				add_prefix_space=__A							,				trim_offsets=__A		)
													SCREAMING_SNAKE_CASE__			=      tokenizer_r(__A							,				return_offsets_mapping=__A							,				add_special_tokens=__A		)
													self.assertEqual(encoding.offset_mapping[0]							,				(0, 1 + len(__A		))		)
													self.assertEqual(
													    encoding.offset_mapping[1]							,				(1 + len(__A		), 1 + len(__A		) + 1 + len(__A		))							,				)






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					field(default="image-classification"				,  metadata={"include_in_asdict_even_if_is_default": True}    )
							lowerCamelCase_           =					Features({"image": Image()}    )
							lowerCamelCase_           =					Features({"labels": ClassLabel}    )
							lowerCamelCase_           =					"image"
							lowerCamelCase_           =					"labels"


							def      _snake_case  (							self    :List[str]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]							,				__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__			=      self.label_schema.copy()
									SCREAMING_SNAKE_CASE__			=      features[self.label_column]
									SCREAMING_SNAKE_CASE__			=      label_schema
									return task_template


							@property
							def      _snake_case  (							self    :Dict		)   ->   Dict[str, str]:



									"""simple docstring"""



									return {
									    self.image_column: "image",
									    self.label_column: "labels",
									}