'''simple docstring'''




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 		__A       (_SCREAMING_SNAKE_CASE ,					unittest.TestCase   ):
       snake_case       :Optional[Any]				    =					RobertaTokenizer
       snake_case       :Union[str, Any]				    =					RobertaTokenizerFast
       snake_case       :Tuple				    =					True
       snake_case       :Any				    =					{"cls_token": "<s>"}






       def 			_snake_case						(      self      ):
              super().setUp()

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

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






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              kwargs.update(self.special_tokens_map      )
              return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **A_      )






       def 			_snake_case						(      self				,     **UpperCamelCase_      ):
              kwargs.update(self.special_tokens_map      )
              return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,     **A_      )






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Tuple       				=		"lower newer"
              __UpperCAmelCase  :				Dict       				=		"lower newer"
              return input_text, output_text






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.tokenizer_class(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map      )
              __UpperCAmelCase  :				List[str]       				=		"lower newer"
              __UpperCAmelCase  :				List[Any]       				=		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
              __UpperCAmelCase  :				Optional[Any]       				=		tokenizer.tokenize(A_      )  # , add_prefix_space=True)
              self.assertListEqual(A_				,     A_      )

              __UpperCAmelCase  :				Optional[int]       				=		tokens + [tokenizer.unk_token]
              __UpperCAmelCase  :				Optional[Any]       				=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(A_      )				,     A_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		self.get_tokenizer()

              self.assertListEqual(tokenizer.encode("Hello world!"				,     add_special_tokens=A_      )				,     [0, 3_14_14, 2_32, 3_28, 2]      )
              self.assertListEqual(
                  tokenizer.encode("Hello world! cécé herlolip 418"				,     add_special_tokens=A_      )				,     [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,     )






       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		self.tokenizer_class.from_pretrained("roberta-base"      )

              __UpperCAmelCase  :				List[Any]       				=		tokenizer.encode("sequence builders"				,     add_special_tokens=A_      )
              __UpperCAmelCase  :				List[Any]       				=		tokenizer.encode("multi-sequence build"				,     add_special_tokens=A_      )

              __UpperCAmelCase  :				Optional[int]       				=		tokenizer.encode(
                  "sequence builders"				,     add_special_tokens=A_				,     add_prefix_space=A_      )
              __UpperCAmelCase  :				List[str]       				=		tokenizer.encode(
                  "sequence builders"				,     "multi-sequence build"				,     add_special_tokens=A_				,     add_prefix_space=A_      )

              __UpperCAmelCase  :				Tuple       				=		tokenizer.build_inputs_with_special_tokens(A_      )
              __UpperCAmelCase  :				Tuple       				=		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      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.get_tokenizer()

              __UpperCAmelCase  :				Dict       				=		"Encode this sequence."
              __UpperCAmelCase  :				Any       				=		tokenizer.byte_encoder[" ".encode("utf-8"      )[0]]

              # Testing encoder arguments
              __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.encode(A_				,     add_special_tokens=A_				,     add_prefix_space=A_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertNotEqual(A_				,     A_      )

              __UpperCAmelCase  :				int       				=		tokenizer.encode(A_				,     add_special_tokens=A_				,     add_prefix_space=A_      )
              __UpperCAmelCase  :				List[Any]       				=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertEqual(A_				,     A_      )

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

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

              __UpperCAmelCase  :				Union[str, Any]       				=		"Encode <mask> sequence"
              __UpperCAmelCase  :				Union[str, Any]       				=		"Encode <mask>sequence"

              __UpperCAmelCase  :				Optional[int]       				=		tokenizer.encode(A_      )
              __UpperCAmelCase  :				Dict       				=		encoded.index(A_      )
              __UpperCAmelCase  :				List[str]       				=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertEqual(A_				,     A_      )

              __UpperCAmelCase  :				Tuple       				=		tokenizer.encode(A_      )
              __UpperCAmelCase  :				Dict       				=		encoded.index(A_      )
              __UpperCAmelCase  :				Union[str, Any]       				=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertNotEqual(A_				,     A_      )






       def 			_snake_case						(      self      ):
              pass






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






       def 			_snake_case						(      self      ):
              for trim_offsets, add_prefix_space in itertools.product([True, False]				,     repeat=2      ):
                     __UpperCAmelCase  :				List[Any]       				=		self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )

                     __UpperCAmelCase  :				Optional[Any]       				=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
                     __UpperCAmelCase  :				Any       				=		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      ):
              # 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  :				List[str]       				=		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                            __UpperCAmelCase  :				List[str]       				=		f"""{text_of_1_token} {text_of_1_token}"""

                            __UpperCAmelCase  :				Dict       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				str       				=		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_      ))				,     )

                            __UpperCAmelCase  :				List[Any]       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				List[str]       				=		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_      ))				,     )

                            __UpperCAmelCase  :				Optional[Any]       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				Tuple       				=		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_      ))				,     )

                            __UpperCAmelCase  :				Tuple       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				Optional[Any]       				=		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_      ))				,     )

                            __UpperCAmelCase  :				Dict       				=		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  :				Tuple       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				List[Any]       				=		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_      ))				,     )

                            __UpperCAmelCase  :				List[Any]       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				Union[str, Any]       				=		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_      ))				,     )

                            __UpperCAmelCase  :				Tuple       				=		self.rust_tokenizer_class.from_pretrained(
                                A_				,     use_fast=A_				,     add_prefix_space=A_				,     trim_offsets=A_      )
                            __UpperCAmelCase  :				int       				=		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_      ))				,     )







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






