




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
 from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileNetVaImageProcessor







class   A(						UpperCamelCase       ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_							=   self.config_class(**self.inputs_dict  )
       self.parent.assertTrue(hasattr(A_						,							'tf_padding'  )  )
       self.parent.assertTrue(hasattr(A_						,							'depth_multiplier'  )  )







class   A:



 '''simple docstring'''


 def __init__(    self :  Dict						,							A_ :  Optional[Any]						,							A_ :  Tuple=13						,							A_ :  Any=3						,							A_ :  Union[str, Any]=32						,							A_ :  List[str]=0.25						,							A_ :  Optional[Any]=8						,							A_ :  str=8						,							A_ :  Union[str, Any]=6						,							A_ :  Optional[Any]=32						,							A_ :  Dict=True						,							A_ :  Optional[Any]=True						,							A_ :  List[Any]=True						,							A_ :  Optional[int]="relu6"						,							A_ :  List[str]=1280						,							A_ :  Optional[Any]=0.1						,							A_ :  List[Any]=0.02						,							A_ :  Any=True						,							A_ :  Union[str, Any]=True						,							A_ :  int=10						,							A_ :  str=None						,							)       ->     Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_							=   parent
       lowerCamelCase_							=   batch_size
       lowerCamelCase_							=   num_channels
       lowerCamelCase_							=   image_size
       lowerCamelCase_							=   depth_multiplier
       lowerCamelCase_							=   depth_divisible_by
       lowerCamelCase_							=   min_depth
       lowerCamelCase_							=   expand_ratio
       lowerCamelCase_							=   tf_padding
       lowerCamelCase_							=   output_stride
       lowerCamelCase_							=   first_layer_is_expansion
       lowerCamelCase_							=   finegrained_output
       lowerCamelCase_							=   hidden_act
       lowerCamelCase_							=   last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier  )
       lowerCamelCase_							=   classifier_dropout_prob
       lowerCamelCase_							=   use_labels
       lowerCamelCase_							=   is_training
       lowerCamelCase_							=   num_labels
       lowerCamelCase_							=   initializer_range
       lowerCamelCase_							=   scope

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

       """simple docstring"""






       lowerCamelCase_							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

       lowerCamelCase_							=   None
       lowerCamelCase_							=   None
       if self.use_labels:
             lowerCamelCase_							=   ids_tensor([self.batch_size]						,							self.num_labels  )
             lowerCamelCase_							=   ids_tensor([self.batch_size, self.image_size, self.image_size]						,							self.num_labels  )

       lowerCamelCase_							=   self.get_config()

       return config, pixel_values, labels, pixel_labels

 def     a__			(    self :  Optional[Any]  )       ->     Tuple:

       """simple docstring"""






       return MobileNetVaConfig(
           num_channels=self.num_channels						,							image_size=self.image_size						,							depth_multiplier=self.depth_multiplier						,							depth_divisible_by=self.depth_divisible_by						,							min_depth=self.min_depth						,							expand_ratio=self.expand_ratio						,							output_stride=self.output_stride						,							first_layer_is_expansion=self.first_layer_is_expansion						,							finegrained_output=self.finegrained_output						,							hidden_act=self.hidden_act						,							tf_padding=self.tf_padding						,							classifier_dropout_prob=self.classifier_dropout_prob						,							initializer_range=self.initializer_range						,							)

 def     a__			(    self :  Union[str, Any]						,							A_ :  Tuple						,							A_ :  Optional[Any]						,							A_ :  Dict						,							A_ :  Optional[int]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   MobileNetVaModel(config=A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(
           result.last_hidden_state.shape						,							(
               self.batch_size,
               self.last_hidden_size,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,							)
       self.parent.assertEqual(
           result.pooler_output.shape						,							(self.batch_size, self.last_hidden_size)						,							)

 def     a__			(    self :  List[str]						,							A_ :  List[str]						,							A_ :  Any						,							A_ :  str						,							A_ :  Optional[int]  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   MobileNetVaForImageClassification(A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_						,							labels=A_  )
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.num_labels)  )

 def     a__			(    self :  List[str]						,							A_ :  Tuple						,							A_ :  Optional[int]						,							A_ :  str						,							A_ :  Optional[int]  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   self.num_labels
       lowerCamelCase_							=   MobileNetVaForSemanticSegmentation(A_  )
       model.to(A_  )
       model.eval()
       lowerCamelCase_							=   model(A_  )
       self.parent.assertEqual(
           result.logits.shape						,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,							)
       lowerCamelCase_							=   model(A_						,							labels=A_  )
       self.parent.assertEqual(
           result.logits.shape						,							(
               self.batch_size,
               self.num_labels,
               self.image_size // self.output_stride,
               self.image_size // self.output_stride,
           )						,							)





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

       """simple docstring"""






       lowerCamelCase_							=   self.prepare_config_and_inputs()
       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   config_and_inputs
       lowerCamelCase_							=   {'pixel_values': pixel_values}
       return config, inputs_dict







@require_torch
class   A(						UpperCamelCase							,				UpperCamelCase							,				unittest.TestCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						(
     (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCamelCase						       =						(
     {
         '''feature-extraction''': MobileNetVaModel,
         '''image-classification''': MobileNetVaForImageClassification,
         '''image-segmentation''': MobileNetVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCamelCase						       =						False
 UpperCamelCase						       =						False
 UpperCamelCase						       =						False
 UpperCamelCase						       =						False

 def     a__			(    self :  Tuple  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   MobileNetVaModelTester(self  )
       lowerCamelCase_							=   MobileNetVaConfigTester(self						,							config_class=A_						,							has_text_modality=A_  )

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

       """simple docstring"""






       self.config_tester.run_common_tests()

 @unittest.skip(reason='MobileNetV2 does not use inputs_embeds'  )
 def     a__			(    self :  Tuple  )       ->     Optional[int]:

       """simple docstring"""






       pass

 @unittest.skip(reason='MobileNetV2 does not support input and output embeddings'  )
 def     a__			(    self :  List[Any]  )       ->     Optional[Any]:

       """simple docstring"""






       pass

 @unittest.skip(reason='MobileNetV2 does not output attentions'  )
 def     a__			(    self :  Optional[Any]  )       ->     List[str]:

       """simple docstring"""






       pass

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

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_							=   ['pixel_values']
             self.assertListEqual(arg_names[:1]						,							A_  )

 def     a__			(    self :  Any  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_  )

 def     a__			(    self :  Optional[int]  )       ->     Dict:

       """simple docstring"""






       def check_hidden_states_output(A_ :  List[Any]						,							A_ :  int						,							A_ :  Union[str, Any]  ):
             lowerCamelCase_							=   model_class(A_  )
             model.to(A_  )
             model.eval()

             with torch.no_grad():
                   lowerCamelCase_							=   model(**self._prepare_for_class(A_						,							A_  )  )

             lowerCamelCase_							=   outputs.hidden_states

             lowerCamelCase_							=   16
             self.assertEqual(len(A_  )						,							A_  )

       lowerCamelCase_					,    lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_							=   True
             check_hidden_states_output(A_						,							A_						,							A_  )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowerCamelCase_							=   True

             check_hidden_states_output(A_						,							A_						,							A_  )

 def     a__			(    self :  Any  )       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_  )

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

       """simple docstring"""






       lowerCamelCase_							=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*A_  )





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

       """simple docstring"""






       for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_							=   MobileNetVaModel.from_pretrained(A_  )
             self.assertIsNotNone(A_  )

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
      return image







@require_torch
@require_vision
class   A(						unittest.TestCase       ):



 '''simple docstring'''


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

       """simple docstring"""






       return (
           MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224'  ) if is_vision_available() else None
       )

 @slow
 def     a__			(    self :  Optional[Any]  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224'  ).to(A_  )

       lowerCamelCase_							=   self.default_image_processor
       lowerCamelCase_							=   prepare_img()
       lowerCamelCase_							=   image_processor(images=A_						,							return_tensors='pt'  ).to(A_  )

       # forward pass
       with torch.no_grad():
             lowerCamelCase_							=   model(**A_  )

       # verify the logits
       lowerCamelCase_							=   torch.Size((1, 1001)  )
       self.assertEqual(outputs.logits.shape						,							A_  )

       lowerCamelCase_							=   torch.tensor([0.2445, -1.1993, 0.1905]  ).to(A_  )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]						,							A_						,							atol=1E-4  )  )





 @slow
 def     a__			(    self :  Optional[int]  )       ->     Dict:

       """simple docstring"""






       lowerCamelCase_							=   MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'  )
       lowerCamelCase_							=   model.to(A_  )

       lowerCamelCase_							=   MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513'  )

       lowerCamelCase_							=   prepare_img()
       lowerCamelCase_							=   image_processor(images=A_						,							return_tensors='pt'  ).to(A_  )

       # forward pass
       with torch.no_grad():
             lowerCamelCase_							=   model(**A_  )
       lowerCamelCase_							=   outputs.logits

       # verify the logits
       lowerCamelCase_							=   torch.Size((1, 21, 65, 65)  )
       self.assertEqual(logits.shape						,							A_  )

       lowerCamelCase_							=   torch.tensor(
           [
               [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
               [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
               [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
           ]						,							device=A_						,							)

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









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



