


"""simple docstring"""







import os
import sys
import unittest


a__    :							str            =					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__    :							Any            =					os.path.join(git_repo_path, '''src''', '''diffusers''')




class 			UpperCamelCase_     (			unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_				(			self      :    int       )	->     Optional[int]:
			__SCREAMING_SNAKE_CASE						       =    find_backend("    if not is_torch_available():"       )
			self.assertEqual(UpperCAmelCase__      ,      "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(UpperCAmelCase__      ,      "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(UpperCAmelCase__      ,      "torch_and_transformers_and_onnx"       )




	def   UpperCAmelCase_				(			self      :    int       )	->     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"      ,      UpperCAmelCase__       )
			self.assertIn("torch_and_transformers"      ,      UpperCAmelCase__       )
			self.assertIn("flax_and_transformers"      ,      UpperCAmelCase__       )
			self.assertIn("torch_and_transformers_and_onnx"      ,      UpperCAmelCase__       )

			# 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      :    List[Any]       )	->     int:
			__SCREAMING_SNAKE_CASE						       =    create_dummy_object("CONSTANT"      ,      "'torch'"       )
			self.assertEqual(UpperCAmelCase__      ,      "\nCONSTANT = None\n"       )

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

			__SCREAMING_SNAKE_CASE						       =    "\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"
			__SCREAMING_SNAKE_CASE						       =    create_dummy_object("FakeClass"      ,      "'torch'"       )
			self.assertEqual(UpperCAmelCase__      ,      UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    List[Any]       )	->     int:
			__SCREAMING_SNAKE_CASE						       =    "# 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"
			__SCREAMING_SNAKE_CASE						       =    create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
			self.assertEqual(dummy_files["torch"]      ,      UpperCAmelCase__       )




"""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,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
