



"""simple docstring"""






import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test

class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					@parameterized.expand([(None,), ('''foo.json''',)]					)
					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  str					)-> Optional[int]:
								lowerCamelCase__		:		Any		     =GenerationConfig(
								    do_sample=lowerCamelCase,  temperature=0.7,  length_penalty=1.0,  bad_words_ids=[[1, 2, 3], [4, 5]],  )
								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(lowerCamelCase,  config_name=lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =GenerationConfig.from_pretrained(lowerCamelCase,  config_name=lowerCamelCase					)

								# Checks parameters that were specified
								self.assertEqual(loaded_config.do_sample,  lowerCamelCase					)
								self.assertEqual(loaded_config.temperature,  0.7					)
								self.assertEqual(loaded_config.length_penalty,  1.0					)
								self.assertEqual(loaded_config.bad_words_ids,  [[1, 2, 3], [4, 5]]					)

								# Checks parameters that were not specified (defaults)
								self.assertEqual(loaded_config.top_k,  50					)
								self.assertEqual(loaded_config.max_length,  20					)
								self.assertEqual(loaded_config.max_time,  lowerCamelCase					)



					def 		snake_case	(	self							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		List[str]		     =AutoConfig.from_pretrained('''gpt2'''					)
								lowerCamelCase__		:		Dict		     =GenerationConfig.from_model_config(lowerCamelCase					)
								lowerCamelCase__		:		Optional[Any]		     =GenerationConfig()

								# The generation config has loaded a few non-default parameters from the model config
								self.assertNotEqual(lowerCamelCase,  lowerCamelCase					)

								# One of those parameters is eos_token_id -- check if it matches
								self.assertNotEqual(generation_config_from_model.eos_token_id,  default_generation_config.eos_token_id					)
								self.assertEqual(generation_config_from_model.eos_token_id,  model_config.eos_token_id					)



					def 		snake_case	(	self							:  Dict					)-> Tuple:
								lowerCamelCase__		:		Dict		     =GenerationConfig()
								lowerCamelCase__		:		Optional[int]		     ={
								    '''max_new_tokens''': 1024,
								    '''foo''': '''bar''',
								}
								lowerCamelCase__		:		Any		     =copy.deepcopy(lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =generation_config.update(**lowerCamelCase					)

								# update_kwargs was not modified (no side effects)
								self.assertEqual(lowerCamelCase,  lowerCamelCase					)

								# update_kwargs was used to update the config on valid attributes
								self.assertEqual(generation_config.max_new_tokens,  1024					)

								# `.update()` returns a dictionary of unused kwargs
								self.assertEqual(lowerCamelCase,  {'''foo''': '''bar'''}					)



					def 		snake_case	(	self							:  Optional[int]					)-> Any:
								lowerCamelCase__		:		List[str]		     =GenerationConfig()
								lowerCamelCase__		:		Optional[int]		     ='''bar'''

								with tempfile.TemporaryDirectory('''test-generation-config'''					) as tmp_dir:
											generation_config.save_pretrained(lowerCamelCase					)

											lowerCamelCase__		:		List[str]		     =GenerationConfig.from_pretrained(lowerCamelCase					)
								# update_kwargs was used to update the config on valid attributes
								self.assertEqual(new_config.foo,  '''bar'''					)

								lowerCamelCase__		:		Union[str, Any]		     =GenerationConfig.from_model_config(lowerCamelCase					)
								assert not hasattr(lowerCamelCase,  '''foo'''					)  # no new kwargs should be initialized if from config




					def 		snake_case	(	self							:  Dict					)-> Optional[Any]:
								lowerCamelCase__		:		Tuple		     =GenerationConfig()
								self.assertEqual(default_config.temperature,  1.0					)
								self.assertEqual(default_config.do_sample,  lowerCamelCase					)
								self.assertEqual(default_config.num_beams,  1					)

								lowerCamelCase__		:		List[str]		     =GenerationConfig(
								    do_sample=lowerCamelCase,  temperature=0.7,  length_penalty=1.0,  bad_words_ids=[[1, 2, 3], [4, 5]],  )
								self.assertEqual(config.temperature,  0.7					)
								self.assertEqual(config.do_sample,  lowerCamelCase					)
								self.assertEqual(config.num_beams,  1					)

								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(lowerCamelCase					)
											lowerCamelCase__		:		Tuple		     =GenerationConfig.from_pretrained(lowerCamelCase,  temperature=1.0					)

								self.assertEqual(loaded_config.temperature,  1.0					)
								self.assertEqual(loaded_config.do_sample,  lowerCamelCase					)
								self.assertEqual(loaded_config.num_beams,  1					)  # default value





@is_staging_test
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					@classmethod
					def 		snake_case	(	cls							:  Tuple					)-> Dict:
								lowerCamelCase__		:		Dict		     =TOKEN
								HfFolder.save_token(lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  Tuple					)-> Dict:
								try:
											delete_repo(token=cls._token,  repo_id='''test-generation-config'''					)
								except HTTPError:
											pass

								try:
											delete_repo(token=cls._token,  repo_id='''valid_org/test-generation-config-org'''					)
								except HTTPError:
											pass



					def 		snake_case	(	self							:  str					)-> Any:
								lowerCamelCase__		:		Union[str, Any]		     =GenerationConfig(
								    do_sample=lowerCamelCase,  temperature=0.7,  length_penalty=1.0,  )
								config.push_to_hub('''test-generation-config''',  use_auth_token=self._token					)

								lowerCamelCase__		:		List[Any]		     =GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''					)
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(lowerCamelCase,  getattr(lowerCamelCase,  lowerCamelCase					)					)

        # Reset repo
								delete_repo(token=self._token,  repo_id='''test-generation-config'''					)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    lowerCamelCase,  repo_id='''test-generation-config''',  push_to_hub=lowerCamelCase,  use_auth_token=self._token					)

								lowerCamelCase__		:		Any		     =GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''					)
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(lowerCamelCase,  getattr(lowerCamelCase,  lowerCamelCase					)					)




					def 		snake_case	(	self							:  Dict					)-> Dict:
								lowerCamelCase__		:		int		     =GenerationConfig(
								    do_sample=lowerCamelCase,  temperature=0.7,  length_penalty=1.0,  )
								config.push_to_hub('''valid_org/test-generation-config-org''',  use_auth_token=self._token					)

								lowerCamelCase__		:		Optional[Any]		     =GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''					)
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(lowerCamelCase,  getattr(lowerCamelCase,  lowerCamelCase					)					)

        # Reset repo
								delete_repo(token=self._token,  repo_id='''valid_org/test-generation-config-org'''					)

								# Push to hub via save_pretrained
								with tempfile.TemporaryDirectory() as tmp_dir:
											config.save_pretrained(
											    lowerCamelCase,  repo_id='''valid_org/test-generation-config-org''',  push_to_hub=lowerCamelCase,  use_auth_token=self._token					)

								lowerCamelCase__		:		str		     =GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''					)
								for k, v in config.to_dict().items():
											if k != "transformers_version":
														self.assertEqual(lowerCamelCase,  getattr(lowerCamelCase,  lowerCamelCase					)					)









"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_lowercase			:			Tuple										=						r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"

@add_start_docstrings(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'rag'
					_a             =							True



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=" / ",  lowerCamelCase							:  Union[str, Any]=" // ",  lowerCamelCase							:  List[Any]=5,  lowerCamelCase							:  int=300,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Tuple="wiki_dpr",  lowerCamelCase							:  Tuple="train",  lowerCamelCase							:  Union[str, Any]="compressed",  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[Any]=None,  **lowerCamelCase							:  str,  )-> List[Any]:
								super().__init__(
								    bos_token_id=lowerCamelCase,  pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  prefix=lowerCamelCase,  vocab_size=lowerCamelCase,  **lowerCamelCase,  )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__		:		Tuple		     =kwargs.pop('''question_encoder'''					)
								lowerCamelCase__		:		int		     =question_encoder_config.pop('''model_type'''					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''generator'''					)
								lowerCamelCase__		:		Tuple		     =decoder_config.pop('''model_type'''					)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__		:		Tuple		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)

								lowerCamelCase__		:		Any		     =reduce_loss
								lowerCamelCase__		:		Tuple		     =label_smoothing
								lowerCamelCase__		:		List[str]		     =exclude_bos_score
								lowerCamelCase__		:		Dict		     =do_marginalize

								lowerCamelCase__		:		Union[str, Any]		     =title_sep
								lowerCamelCase__		:		Dict		     =doc_sep
								lowerCamelCase__		:		List[Any]		     =n_docs
								lowerCamelCase__		:		List[str]		     =max_combined_length

								lowerCamelCase__		:		List[Any]		     =dataset
								lowerCamelCase__		:		int		     =dataset_split
								lowerCamelCase__		:		List[Any]		     =index_name

								lowerCamelCase__		:		int		     =retrieval_vector_size
								lowerCamelCase__		:		Dict		     =retrieval_batch_size
								lowerCamelCase__		:		str		     =passages_path
								lowerCamelCase__		:		Any		     =index_path
								lowerCamelCase__		:		List[Any]		     =use_dummy_dataset

								lowerCamelCase__		:		Optional[int]		     =output_retrieved

								lowerCamelCase__		:		List[str]		     =do_deduplication

								lowerCamelCase__		:		Tuple		     =use_cache

								if self.forced_eos_token_id is None:
											lowerCamelCase__		:		int		     =getattr(self.generator,  '''forced_eos_token_id''',  lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  lowerCamelCase							:  PretrainedConfig,  lowerCamelCase							:  PretrainedConfig,  **lowerCamelCase							:  int					)-> PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict(),  generator=generator_config.to_dict(),  **lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Optional[int]		     =self.question_encoder.to_dict()
								lowerCamelCase__		:		Tuple		     =self.generator.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =self.__class__.model_type
								return output




