



'''simple docstring'''





import copy

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


SCREAMING_SNAKE_CASE__      					=     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 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"rag"
       lowerCAmelCase__  :Any						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=True     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=" / "     ,UpperCAmelCase_=" // "     ,UpperCAmelCase_=5     ,UpperCAmelCase_=300     ,UpperCAmelCase_=768     ,UpperCAmelCase_=8     ,UpperCAmelCase_="wiki_dpr"     ,UpperCAmelCase_="train"     ,UpperCAmelCase_="compressed"     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=False     ,UpperCAmelCase_=False     ,UpperCAmelCase_=0.0     ,UpperCAmelCase_=True     ,UpperCAmelCase_=False     ,UpperCAmelCase_=False     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,UpperCAmelCase_=None     ,**UpperCAmelCase_     ,)  ->							int:
           super().__init__(
               bos_token_id=UpperCAmelCase_     ,pad_token_id=UpperCAmelCase_     ,eos_token_id=UpperCAmelCase_     ,decoder_start_token_id=UpperCAmelCase_     ,forced_eos_token_id=UpperCAmelCase_     ,is_encoder_decoder=UpperCAmelCase_     ,prefix=UpperCAmelCase_     ,vocab_size=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           assert (
               "question_encoder" in kwargs and "generator" in kwargs
           ), "Config has to be initialized with question_encoder and generator config"
           lowercase__		       =						kwargs.pop("question_encoder"      )
           lowercase__		       =						question_encoder_config.pop("model_type"      )
           lowercase__		       =						kwargs.pop("generator"      )
           lowercase__		       =						decoder_config.pop("model_type"      )

           from ..auto.configuration_auto import AutoConfig

           lowercase__		       =						AutoConfig.for_model(UpperCAmelCase_     ,**UpperCAmelCase_      )
           lowercase__		       =						AutoConfig.for_model(UpperCAmelCase_     ,**UpperCAmelCase_      )

           lowercase__		       =						reduce_loss
           lowercase__		       =						label_smoothing
           lowercase__		       =						exclude_bos_score
           lowercase__		       =						do_marginalize

           lowercase__		       =						title_sep
           lowercase__		       =						doc_sep
           lowercase__		       =						n_docs
           lowercase__		       =						max_combined_length

           lowercase__		       =						dataset
           lowercase__		       =						dataset_split
           lowercase__		       =						index_name

           lowercase__		       =						retrieval_vector_size
           lowercase__		       =						retrieval_batch_size
           lowercase__		       =						passages_path
           lowercase__		       =						index_path
           lowercase__		       =						use_dummy_dataset

           lowercase__		       =						output_retrieved

           lowercase__		       =						do_deduplication

           lowercase__		       =						use_cache

           if self.forced_eos_token_id is None:
               lowercase__		       =						getattr(self.generator     ,"forced_eos_token_id"     ,UpperCAmelCase_      )



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							PretrainedConfig:
           return cls(question_encoder=question_encoder_config.to_dict()     ,generator=generator_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.question_encoder.to_dict()
           lowercase__		       =						self.generator.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output









'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
 import jax

 from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=100     ,UpperCAmelCase_=13     ,UpperCAmelCase_=30     ,UpperCAmelCase_=2     ,UpperCAmelCase_=3     ,UpperCAmelCase_=True     ,UpperCAmelCase_=True     ,UpperCAmelCase_=32     ,UpperCAmelCase_=5     ,UpperCAmelCase_=4     ,UpperCAmelCase_=37     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=10     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=3     ,)  ->							List[Any]:
           lowercase__		       =						parent
           lowercase__		       =						vocab_size
           lowercase__		       =						batch_size
           lowercase__		       =						image_size
           lowercase__		       =						patch_size
           lowercase__		       =						num_channels
           lowercase__		       =						is_training
           lowercase__		       =						use_labels
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						type_sequence_label_size
           lowercase__		       =						initializer_range

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           lowercase__		       =						(image_size // patch_size) ** 2
           lowercase__		       =						num_patches + 1



       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

           lowercase__		       =						None
           if self.use_labels:
               lowercase__		       =						ids_tensor([self.batch_size]     ,self.type_sequence_label_size      )

           lowercase__		       =						BeitConfig(
               vocab_size=self.vocab_size     ,image_size=self.image_size     ,patch_size=self.patch_size     ,num_channels=self.num_channels     ,hidden_size=self.hidden_size     ,num_hidden_layers=self.num_hidden_layers     ,num_attention_heads=self.num_attention_heads     ,intermediate_size=self.intermediate_size     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,is_decoder=UpperCAmelCase_     ,initializer_range=self.initializer_range     ,)

           return config, pixel_values, labels



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitModel(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Tuple:
           lowercase__		       =						FlaxBeitForMaskedImageModeling(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[str]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FlaxBeitForImageClassification(config=UpperCAmelCase_      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FlaxBeitForImageClassification(UpperCAmelCase_      )

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_      )







       def 							_a      (				self      )  ->							int:
           lowercase__		       =						self.prepare_config_and_inputs()
           (
               (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       (
               lowercase__
           )       ,       
           )		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_flax
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Optional[int]						   =					(
           (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
       )



       def 							_a      (				self      )  ->							None:
           lowercase__		       =						FlaxBeitModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,has_text_modality=UpperCAmelCase_     ,hidden_size=37      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           self.config_tester.run_common_tests()



       def 							_a      (				self      )  ->							Optional[Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               lowercase__		       =						inspect.signature(model.__call__      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__		       =						[*signature.parameters.keys()]

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__      ):
                   lowercase__		       =						self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_      )
                   lowercase__		       =						model_class(UpperCAmelCase_      )

                   @jax.jit
                   def model_jitted(UpperCAmelCase_     ,**UpperCAmelCase_      ):
                       return model(pixel_values=UpperCAmelCase_     ,**UpperCAmelCase_      )

                   with self.subTest("JIT Enabled"      ):
                       lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   with self.subTest("JIT Disabled"      ):
                       with jax.disable_jit():
                           lowercase__		       =						model_jitted(**UpperCAmelCase_      ).to_tuple()

                   self.assertEqual(len(UpperCAmelCase_      )     ,len(UpperCAmelCase_      )      )
                   for jitted_output, output in zip(UpperCAmelCase_     ,UpperCAmelCase_      ):
                       self.assertEqual(jitted_output.shape     ,output.shape      )



       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Tuple:
           for model_class_name in self.all_model_classes:
               lowercase__		       =						model_class_name.from_pretrained("microsoft/beit-base-patch16-224"      )
               lowercase__		       =						model(np.ones((1, 3, 224, 224)      )      )
               self.assertIsNotNone(UpperCAmelCase_      )





def        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
    return image



@require_vision
@require_flax
class 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Optional[int]:
           return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None



       @slow
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      ).pixel_values

           # prepare bool_masked_pos
           lowercase__		       =						np.ones((1, 196)     ,dtype=UpperCAmelCase_      )

           # forward pass
           lowercase__		       =						model(pixel_values=UpperCAmelCase_     ,bool_masked_pos=UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 196, 8_192)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array(
               [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]      )

           self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]     ,UpperCAmelCase_     ,atol=1E-2      )      )



       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 1_000)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([-1.23_85, -1.09_87, -1.01_08]      )

           self.assertTrue(np.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						281
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )







       @slow
       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      )

           lowercase__		       =						self.default_image_processor
           lowercase__		       =						prepare_img()
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="np"      )

           # forward pass
           lowercase__		       =						model(**UpperCAmelCase_      )
           lowercase__		       =						outputs.logits

           # verify the logits
           lowercase__		       =						(1, 21_841)
           self.assertEqual(logits.shape     ,UpperCAmelCase_      )

           lowercase__		       =						np.array([1.68_81, -0.27_87, 0.59_01]      )

           self.assertTrue(np.allclose(logits[0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           lowercase__		       =						2_396
           self.assertEqual(logits.argmax(-1      ).item()     ,UpperCAmelCase_      )




