




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()







class   A(						UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						PriorTransformer
 UpperCamelCase						       =						'''hidden_states'''

 @property
 def     a__			(    self :  Union[str, Any]  )       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   4
       lowerCamelCase_							=   8
       lowerCamelCase_							=   7

       lowerCamelCase_							=   floats_tensor((batch_size, embedding_dim)  ).to(A_  )

       lowerCamelCase_							=   floats_tensor((batch_size, embedding_dim)  ).to(A_  )
       lowerCamelCase_							=   floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(A_  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }

 def     a__			(    self :  Tuple						,							A_ :  Dict=0  )       ->     Dict:

       """simple docstring"""






       torch.manual_seed(A_  )
       lowerCamelCase_							=   4
       lowerCamelCase_							=   8
       lowerCamelCase_							=   7

       lowerCamelCase_							=   torch.randn((batch_size, embedding_dim)  ).to(A_  )

       lowerCamelCase_							=   torch.randn((batch_size, embedding_dim)  ).to(A_  )
       lowerCamelCase_							=   torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(A_  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }

 @property
 def     a__			(    self :  List[str]  )       ->     str:

       """simple docstring"""






       return (4, 8)

 @property
 def     a__			(    self :  Dict  )       ->     Dict:

       """simple docstring"""






       return (4, 8)

 def     a__			(    self :  Tuple  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   {
           'num_attention_heads': 2,
           'attention_head_dim': 4,
           'num_layers': 2,
           'embedding_dim': 8,
           'num_embeddings': 7,
           'additional_embeddings': 4,
       }
       lowerCamelCase_							=   self.dummy_input
       return init_dict, inputs_dict

 def     a__			(    self :  Tuple  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   PriorTransformer.from_pretrained(
           'hf-internal-testing/prior-dummy'						,							output_loading_info=A_  )
       self.assertIsNotNone(A_  )
       self.assertEqual(len(loading_info['missing_keys']  )						,							0  )

       model.to(A_  )
       lowerCamelCase_							=   model(**self.dummy_input  )[0]

       assert hidden_states is not None, "Make sure output is not None"

 def     a__			(    self :  Dict  )       ->     str:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.prepare_init_args_and_inputs_for_common()

       lowerCamelCase_							=   self.model_class(**A_  )
       lowerCamelCase_							=   inspect.signature(model.forward  )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       lowerCamelCase_							=   [*signature.parameters.keys()]

       lowerCamelCase_							=   ['hidden_states', 'timestep']
       self.assertListEqual(arg_names[:2]						,							A_  )





 def     a__			(    self :  List[Any]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'  )
       lowerCamelCase_							=   model.to(A_  )

       if hasattr(A_						,							'set_default_attn_processor'  ):
             model.set_default_attn_processor()

       lowerCamelCase_							=   self.get_dummy_seed_input()

       with torch.no_grad():
             lowerCamelCase_							=   model(**A_  )[0]

       lowerCamelCase_							=   output[0, :5].flatten().cpu()
       print(A_  )

       # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
       # the expected output slices are not the same for CPU and GPU.
       lowerCamelCase_							=   torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]  )
       self.assertTrue(torch_all_close(A_						,							A_						,							rtol=1E-2  )  )







@slow
class   A(						unittest.TestCase       ):



 '''simple docstring'''


 def     a__			(    self :  Any						,							A_ :  List[Any]=1						,							A_ :  Any=768						,							A_ :  int=77						,							A_ :  int=0  )       ->     List[str]:

       """simple docstring"""






       torch.manual_seed(A_  )
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   embedding_dim
       lowerCamelCase_							=   num_embeddings

       lowerCamelCase_							=   torch.randn((batch_size, embedding_dim)  ).to(A_  )

       lowerCamelCase_							=   torch.randn((batch_size, embedding_dim)  ).to(A_  )
       lowerCamelCase_							=   torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(A_  )

       return {
           "hidden_states": hidden_states,
           "timestep": 2,
           "proj_embedding": proj_embedding,
           "encoder_hidden_states": encoder_hidden_states,
       }

 def     a__			(    self :  Union[str, Any]  )       ->     Optional[Any]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 @parameterized.expand(
     [
         # fmt: off
         [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
         [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
         # fmt: on
     ]  )
 def     a__			(    self :  int						,							A_ :  Dict						,							A_ :  str  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'						,							subfolder='prior'  )
       model.to(A_  )
       lowerCamelCase_							=   self.get_dummy_seed_input(seed=A_  )

       with torch.no_grad():
             lowerCamelCase_							=   model(**A_  )[0]

       assert list(sample.shape  ) == [1, 768]

       lowerCamelCase_							=   sample[0, :8].flatten().cpu()
       print(A_  )
       lowerCamelCase_							=   torch.tensor(A_  )

       assert torch_all_close(A_						,							A_						,							atol=1E-3  )









import copy

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


lowerCamelCase    :		Any			   =       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(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''rag'''
 UpperCamelCase						       =						True

 def __init__(    self :  Optional[Any]						,							A_ :  Optional[Any]=None						,							A_ :  Any=True						,							A_ :  Dict=None						,							A_ :  Optional[int]=None						,							A_ :  str=None						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  List[str]=" / "						,							A_ :  Tuple=" // "						,							A_ :  Union[str, Any]=5						,							A_ :  Optional[Any]=300						,							A_ :  int=768						,							A_ :  Dict=8						,							A_ :  int="wiki_dpr"						,							A_ :  int="train"						,							A_ :  List[str]="compressed"						,							A_ :  Tuple=None						,							A_ :  Optional[Any]=None						,							A_ :  Optional[int]=False						,							A_ :  str=False						,							A_ :  Optional[Any]=0.0						,							A_ :  Union[str, Any]=True						,							A_ :  List[Any]=False						,							A_ :  Union[str, Any]=False						,							A_ :  Dict=False						,							A_ :  str=True						,							A_ :  List[str]=None						,							**A_ :  Optional[Any]						,							)       ->     str:

       """simple docstring"""






       super().__init__(
           bos_token_id=A_						,							pad_token_id=A_						,							eos_token_id=A_						,							decoder_start_token_id=A_						,							forced_eos_token_id=A_						,							is_encoder_decoder=A_						,							prefix=A_						,							vocab_size=A_						,							**A_						,							)
       assert (
           "question_encoder" in kwargs and "generator" in kwargs
       ), "Config has to be initialized with question_encoder and generator config"
       lowerCamelCase_							=   kwargs.pop('question_encoder'  )
       lowerCamelCase_							=   question_encoder_config.pop('model_type'  )
       lowerCamelCase_							=   kwargs.pop('generator'  )
       lowerCamelCase_							=   decoder_config.pop('model_type'  )

       from ..auto.configuration_auto import AutoConfig

       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )
       lowerCamelCase_							=   AutoConfig.for_model(A_						,							**A_  )

       lowerCamelCase_							=   reduce_loss
       lowerCamelCase_							=   label_smoothing
       lowerCamelCase_							=   exclude_bos_score
       lowerCamelCase_							=   do_marginalize

       lowerCamelCase_							=   title_sep
       lowerCamelCase_							=   doc_sep
       lowerCamelCase_							=   n_docs
       lowerCamelCase_							=   max_combined_length

       lowerCamelCase_							=   dataset
       lowerCamelCase_							=   dataset_split
       lowerCamelCase_							=   index_name

       lowerCamelCase_							=   retrieval_vector_size
       lowerCamelCase_							=   retrieval_batch_size
       lowerCamelCase_							=   passages_path
       lowerCamelCase_							=   index_path
       lowerCamelCase_							=   use_dummy_dataset

       lowerCamelCase_							=   output_retrieved

       lowerCamelCase_							=   do_deduplication

       lowerCamelCase_							=   use_cache

       if self.forced_eos_token_id is None:
             lowerCamelCase_							=   getattr(self.generator						,							'forced_eos_token_id'						,							A_  )

 @classmethod
 def     a__			(    cls :  str						,							A_ :  PretrainedConfig						,							A_ :  PretrainedConfig						,							**A_ :  List[str]  )       ->     PretrainedConfig:

       """simple docstring"""






       return cls(question_encoder=question_encoder_config.to_dict()						,							generator=generator_config.to_dict()						,							**A_  )





 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   copy.deepcopy(self.__dict__  )
       lowerCamelCase_							=   self.question_encoder.to_dict()
       lowerCamelCase_							=   self.generator.to_dict()
       lowerCamelCase_							=   self.__class__.model_type
       return output



