




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		__A						:							Optional[Any]							=  None

__A						:							Tuple							=  logging.get_logger(__name__)
__A						:							Any							=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							Union[str, Any]							=  {
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

__A						:							Optional[Any]							=  {
    """google/fnet-base""": 5_1_2,
    """google/fnet-large""": 5_1_2,
}

__A						:							Optional[Any]							=  """▁"""




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     ['''input_ids''', '''token_type_ids''']
							a__						  =     FNetTokenizer





							def __init__(					self       :	Optional[int]       ,     a       :	str=None       ,     a       :	Tuple=None       ,     a       :	Tuple=False       ,     a       :	Tuple=True       ,     a       :	Optional[Any]=True       ,     a       :	Optional[int]="<unk>"       ,     a       :	Dict="[SEP]"       ,     a       :	Optional[Any]="<pad>"       ,     a       :	Union[str, Any]="[CLS]"       ,     a       :	Optional[int]="[MASK]"       ,     **a       :	Optional[Any]       ,     )			->					Optional[Any]:
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										SCREAMING_SNAKE_CASE           =					(
										    AddedToken(a       ,     lstrip=a       ,     rstrip=a       ,     normalized=a			)
										    if isinstance(a       ,     a			)
										    else mask_token
										)

										super().__init__(
										    a       ,     tokenizer_file=a       ,     do_lower_case=a       ,     remove_space=a       ,     keep_accents=a       ,     unk_token=a       ,     sep_token=a       ,     pad_token=a       ,     cls_token=a       ,     mask_token=a       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					do_lower_case
										SCREAMING_SNAKE_CASE           =					remove_space
										SCREAMING_SNAKE_CASE           =					keep_accents
										SCREAMING_SNAKE_CASE           =					vocab_file
										SCREAMING_SNAKE_CASE           =					False if not self.vocab_file else True





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]
										if token_ids_a is None:
													return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[self.cls_token_id]

										if token_ids_a is None:
													return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep			) * [0] + len(token_ids_a + sep			) * [1]







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(a			):
													copyfile(self.vocab_file       ,     a			)

										return (out_vocab_file,)









import os
import sys
import unittest


__A						:							Any							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__A						:							List[Any]							=  os.path.join(git_repo_path, """src""", """diffusers""")




class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					find_backend("""    if not is_torch_available():"""			)
										self.assertEqual(a       ,     """torch"""			)

										# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
										# self.assertEqual(backend_with_underscore, "tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend("""    if not (is_torch_available() and is_transformers_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers"""			)

										# double_backend_with_underscore = find_backend(
										#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
										# )
										# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

										SCREAMING_SNAKE_CASE           =					find_backend(
										    """    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"""			)
										self.assertEqual(a       ,     """torch_and_transformers_and_onnx"""			)





							def    _UpperCAmelCase     (					self       :	List[str]			)			->					int:
										SCREAMING_SNAKE_CASE           =					read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn("""torch"""       ,     a			)
										self.assertIn("""torch_and_transformers"""       ,     a			)
										self.assertIn("""flax_and_transformers"""       ,     a			)
										self.assertIn("""torch_and_transformers_and_onnx"""       ,     a			)

										# Likewise, we can't assert on the exact content of a key
										self.assertIn("""UNet2DModel"""       ,     objects["""torch"""]			)
										self.assertIn("""FlaxUNet2DConditionModel"""       ,     objects["""flax"""]			)
										self.assertIn("""StableDiffusionPipeline"""       ,     objects["""torch_and_transformers"""]			)
										self.assertIn("""FlaxStableDiffusionPipeline"""       ,     objects["""flax_and_transformers"""]			)
										self.assertIn("""LMSDiscreteScheduler"""       ,     objects["""torch_and_scipy"""]			)
										self.assertIn("""OnnxStableDiffusionPipeline"""       ,     objects["""torch_and_transformers_and_onnx"""]			)





							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""CONSTANT"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     """\nCONSTANT = None\n"""			)

										SCREAMING_SNAKE_CASE           =					create_dummy_object("""function"""       ,     """'torch'"""			)
										self.assertEqual(
										    a       ,     """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""			)

										SCREAMING_SNAKE_CASE           =					"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, 'torch')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, 'torch')
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_object("""FakeClass"""       ,     """'torch'"""			)
										self.assertEqual(a       ,     a			)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, [\"torch\"])
"""
										SCREAMING_SNAKE_CASE           =					create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}			)
										self.assertEqual(dummy_files["""torch"""]       ,     a			)






