
"""simple docstring"""


import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Dict   =			CLIPTokenizer
       UpperCamelCase_		:					Optional[int]   =			CLIPTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Optional[int]   =			{}
       UpperCamelCase_		:					Tuple   =			False






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

              # fmt: off
              UpperCamelCase					 =		["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
              # fmt: on
              UpperCamelCase					 =		dict(zip(UpperCAmelCase_       ,      range(len(UpperCAmelCase_			)			)			)			)
              UpperCamelCase					 =		["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
              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(UpperCAmelCase_			) + "\n"			)
              with open(self.merges_file       ,      "w"       ,      encoding="utf-8"			) as fp:
                     fp.write("\n".join(UpperCAmelCase_			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      **UpperCAmelCase_ :							Any			)->  Union[str, Any]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return CLIPTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      **UpperCAmelCase_ :							List[str]			)->  Optional[int]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		"lower newer"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		CLIPTokenizer(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map			)
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokens + [tokenizer.unk_token]
              UpperCamelCase					 =		[10, 2, 16, 9, 3, 2, 16, 20]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       @require_ftfy
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		self.tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)

                            UpperCamelCase					 =		"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
                            UpperCamelCase					 =		tokenizer_s.tokenize(UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r.tokenize(UpperCAmelCase_			)

                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

                            # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                            # with Tilde) encoded in 2 different ways
                            UpperCamelCase					 =		"xa\u0303y" + " " + "x\xe3y"
                            UpperCamelCase					 =		tokenizer_s.tokenize(UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r.tokenize(UpperCAmelCase_			)

                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

                            # Test that the tokenization is identical on unicode of space type
                            UpperCamelCase					 =		[
                                "\u0009",  # (horizontal tab, '\t')
                                "\u000B",  # (vertical tab)
                                "\u000C",  # (form feed)
                                "\u0020",  # (space, ' ')
                                "\u200E",  # (left-to-right mark):w
                                "\u200F",  # (right-to-left mark)
                            ]
                            for unicode_seq in spaces_unicodes:
                                   UpperCamelCase					 =		tokenizer_s.tokenize(UpperCAmelCase_			)
                                   UpperCamelCase					 =		tokenizer_r.tokenize(UpperCAmelCase_			)

                                   self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

                            # Test that the tokenization is identical on unicode of line break type
                            UpperCamelCase					 =		[
                                "\u000A",  # (line feed, '\n')
                                "\r\n",  # (carriage return and line feed, '\r\n')
                                "\u000D",  # (carriage return, '\r')
                                "\r",  # (carriage return, '\r')
                                "\u000D",  # (carriage return, '\r')
                                "\u2028",  # (line separator)
                                "\u2029",  # (paragraph separator)
                                # "\u0085", # (next line)
                            ]

                            # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                            # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                            # space (and thus into an empty list).

                            for unicode_seq in line_break_unicodes:
                                   UpperCamelCase					 =		tokenizer_s.tokenize(UpperCAmelCase_			)
                                   UpperCamelCase					 =		tokenizer_r.tokenize(UpperCAmelCase_			)

                                   self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






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







              """simple docstring"""
              # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                            UpperCamelCase					 =		f"{text_of_1_token} {text_of_1_token}"

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      )
                            UpperCamelCase					 =		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					 =		f" {text}"

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      )
                            UpperCamelCase					 =		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_			))       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Optional[int]:







              """simple docstring"""
              # Test related to the breaking change introduced in transformers v4.17.0
              # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
              with self.assertRaises(UpperCAmelCase_			) as context:
                     self.rust_tokenizer_class.from_pretrained("robot-test/old-clip-tokenizer"			)

              self.assertTrue(
                  context.exception.args[0].startswith(
                      "The `backend_tokenizer` provided does not match the expected format."			)			)






       @require_ftfy
       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Any:







              """simple docstring"""
              super().test_tokenization_python_rust_equals()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  str:







              """simple docstring"""
              # CLIP always lower cases letters
              pass







"""simple docstring"""


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





