'''simple docstring'''

import os
import sys
import unittest


lowercase_     		= 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
lowercase_     		= os.path.join(git_repo_path, '''src''', '''diffusers''')
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_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 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_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 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_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 		UpperCamelCase__       (   self				) ->     str:
								_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				)






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

								_lowerCAmelCase										=vocab_size
								_lowerCAmelCase										=hidden_size
								_lowerCAmelCase										=num_hidden_layers
								_lowerCAmelCase										=num_attention_heads
								_lowerCAmelCase										=hidden_act
								_lowerCAmelCase										=intermediate_size
								_lowerCAmelCase										=hidden_dropout_prob
								_lowerCAmelCase										=attention_probs_dropout_prob
								_lowerCAmelCase										=max_position_embeddings
								_lowerCAmelCase										=type_vocab_size
								_lowerCAmelCase										=initializer_range
								_lowerCAmelCase										=layer_norm_eps
								_lowerCAmelCase										=position_embedding_type
								_lowerCAmelCase										=use_cache
								_lowerCAmelCase										=classifier_dropout

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   self				) ->     Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
														_lowerCAmelCase										={0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
														_lowerCAmelCase										={0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]				)





