




'''simple docstring'''






import os
import sys
import unittest


A_			      =		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_			      =		os.path.join(git_repo_path, "src", "transformers")

A_			      =		'\n{0} = None\n'

A_			      =		'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


A_			      =		'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






class        lowercase_      (						unittest.TestCase							):





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   int	      =    find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
           self.assertIsNone(A_			)

           snake_case__       :   str	      =    find_backend('    if not is_tokenizers_available():'			)
           self.assertEqual(A_     ,							'tokenizers'			)

           snake_case__       :   int	      =    find_backend('    if not is_tensorflow_text_available():'			)
           self.assertEqual(A_     ,							'tensorflow_text'			)

           snake_case__       :   int	      =    find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
           self.assertEqual(A_     ,							'sentencepiece_and_tokenizers'			)

           snake_case__       :   Any	      =    find_backend(
               '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
           self.assertEqual(A_     ,							'sentencepiece_and_tensorflow_text'			)

           snake_case__       :   Optional[Any]	      =    find_backend(
               '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
           self.assertEqual(A_     ,							'sentencepiece_and_tokenizers_and_vision'			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Union[str, Any]	      =    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('tensorflow_text'     ,							A_			)
           self.assertIn('sentencepiece_and_tokenizers'     ,							A_			)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('BertModel'     ,							objects['torch']			)
           self.assertIn('TFBertModel'     ,							objects['tf']			)
           self.assertIn('FlaxBertModel'     ,							objects['flax']			)
           self.assertIn('BertModel'     ,							objects['torch']			)
           self.assertIn('TFBertTokenizer'     ,							objects['tensorflow_text']			)
           self.assertIn('convert_slow_tokenizer'     ,							objects['sentencepiece_and_tokenizers']			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Optional[Any]	      =    create_dummy_object('CONSTANT'     ,							'\'torch\''			)
           self.assertEqual(A_     ,							'\nCONSTANT = None\n'			)

           snake_case__       :   Optional[int]	      =    create_dummy_object('function'     ,							'\'torch\''			)
           self.assertEqual(
               A_     ,							'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

           snake_case__       :   int	      =    '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
           snake_case__       :   List[str]	      =    create_dummy_object('FakeClass'     ,							'\'torch\''			)
           self.assertEqual(A_     ,							A_			)





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Any	      =    '# 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'
           snake_case__       :   Any	      =    create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
           self.assertEqual(dummy_files['torch']     ,							A_			)


'''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

lowerCAmelCase					:    Tuple           = TaTokenizer

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

lowerCAmelCase					:    Optional[int]           = TaTokenizerFast

lowerCAmelCase					:    Any           = {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

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

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

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    Optional[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

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




