




import os
import sys
import unittest


UpperCamelCase__					    =    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
UpperCamelCase__					    =    os.path.join(git_repo_path, """src""", """diffusers""")
class     a__					(      unittest.TestCase   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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")

											__lowerCAmelCase            = 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")

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






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = create_dummy_object("CONSTANT"	,    "'torch'"    )
											self.assertEqual(_A	,    "\nCONSTANT = None\n"    )

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

											__lowerCAmelCase            = "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
											__lowerCAmelCase            = create_dummy_object("FakeClass"	,    "'torch'"    )
											self.assertEqual(_A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
											__lowerCAmelCase            = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}    )
											self.assertEqual(dummy_files["torch"]	,    _A    )










import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class     a__					(      snake_case__   ):






						def __init__(     self	,    _A = "▁"	,    _A = True	,    _A = "<unk>"	,    _A = "</s>"	,    _A = "<pad>"	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = {
											    "pad": {"id": 0, "token": pad_token},
											    "eos": {"id": 1, "token": eos_token},
											    "unk": {"id": 2, "token": unk_token},
											}

											__lowerCAmelCase            = [None] * len(self.special_tokens    )
											for token_dict in self.special_tokens.values():
																__lowerCAmelCase            = token_dict["token"]

											__lowerCAmelCase            = Tokenizer(Unigram()    )

											__lowerCAmelCase            = normalizers.Sequence(
											    [
											        normalizers.Nmt(),
											        normalizers.NFKC(),
											        normalizers.Replace(Regex(" {2,}"    )	,    " "    ),
											        normalizers.Lowercase(),
											    ]    )
											__lowerCAmelCase            = pre_tokenizers.Sequence(
											    [
											        pre_tokenizers.Metaspace(replacement=_A	,    add_prefix_space=_A    ),
											        pre_tokenizers.Digits(individual_digits=_A    ),
											        pre_tokenizers.Punctuation(),
											    ]    )
											__lowerCAmelCase            = decoders.Metaspace(replacement=_A	,    add_prefix_space=_A    )

											__lowerCAmelCase            = TemplateProcessing(
											    single=f"""$A {self.special_tokens['eos']['token']}"""	,    special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]	,    )

											__lowerCAmelCase            = {
											    "model": "SentencePieceUnigram",
											    "replacement": replacement,
											    "add_prefix_space": add_prefix_space,
											}

											super().__init__(_A	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											if isinstance(_A	,    _A    ):
																__lowerCAmelCase            = [files]
											self._tokenizer.train(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = 8_0_0_0	,    _A = True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = trainers.UnigramTrainer(
											    vocab_size=_A	,    special_tokens=self.special_tokens_list	,    show_progress=_A	,    )

											self._tokenizer.train_from_iterator(_A	,    trainer=_A    )

											self.add_unk_id()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = json.loads(self._tokenizer.to_str()    )

											__lowerCAmelCase            = self.special_tokens["unk"]["id"]

											__lowerCAmelCase            = Tokenizer.from_str(json.dumps(_A    )    )




