
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", "diffusers")







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   int       )						->						Optional[int]:
         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")

         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")

         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				:   int       )						->						Any:
         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				:   Dict       )						->						int:
         snake_case								= create_dummy_object("CONSTANT"    ,   "'torch'"       )
         self.assertEqual(_A    ,   "\nCONSTANT = None\n"       )

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

         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"
         snake_case								= create_dummy_object("FakeClass"    ,   "'torch'"       )
         self.assertEqual(_A    ,   _A       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         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"
         snake_case								= create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
         self.assertEqual(dummy_files["torch"]    ,   _A       )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							str									=		"realm"





     def __init__(self				:   Optional[int]    ,   _A				:   Optional[Any]=3_0_5_2_2    ,   _A				:   Tuple=7_6_8    ,   _A				:   List[str]=1_2_8    ,   _A				:   Optional[Any]=1_2    ,   _A				:   Dict=1_2    ,   _A				:   Tuple=8    ,   _A				:   Dict=3_0_7_2    ,   _A				:   Union[str, Any]="gelu_new"    ,   _A				:   Any=0.1    ,   _A				:   int=0.1    ,   _A				:   Union[str, Any]=5_1_2    ,   _A				:   List[str]=2    ,   _A				:   Any=0.02    ,   _A				:   int=1E-12    ,   _A				:   Tuple=2_5_6    ,   _A				:   Optional[Any]=1_0    ,   _A				:   Any=1E-3    ,   _A				:   int=5    ,   _A				:   int=3_2_0    ,   _A				:   Dict=1_3_3_5_3_7_1_8    ,   _A				:   Any=5_0_0_0    ,   _A				:   Union[str, Any]=1    ,   _A				:   Dict=0    ,   _A				:   int=2    ,   **_A				:   Union[str, Any]    ,   )						->						Optional[Any]:
         super().__init__(pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   **_A       )

         # Common config
         snake_case								= vocab_size
         snake_case								= max_position_embeddings
         snake_case								= hidden_size
         snake_case								= retriever_proj_size
         snake_case								= num_hidden_layers
         snake_case								= num_attention_heads
         snake_case								= num_candidates
         snake_case								= intermediate_size
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= initializer_range
         snake_case								= type_vocab_size
         snake_case								= layer_norm_eps

         # Reader config
         snake_case								= span_hidden_size
         snake_case								= max_span_width
         snake_case								= reader_layer_norm_eps
         snake_case								= reader_beam_size
         snake_case								= reader_seq_len

         # Retrieval config
         snake_case								= num_block_records
         snake_case								= searcher_beam_size

