






"""simple docstring"""


import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
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


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
       from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VivitImageProcessor


class    snake_case						:


   '''simple docstring'''

   def __init__(      self					:	Optional[Any],			_lowerCamelCase					:	int,			_lowerCamelCase					:	Any=2,			_lowerCamelCase					:	Tuple=True,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	int=10,			_lowerCamelCase					:	Optional[int]=10,			_lowerCamelCase					:	List[str]=8,			_lowerCamelCase					:	int=[2, 4, 4],			_lowerCamelCase					:	Any=3,			_lowerCamelCase					:	List[str]=7_68,			_lowerCamelCase					:	Dict=5,			_lowerCamelCase					:	Optional[int]=4,			_lowerCamelCase					:	List[str]=37,			_lowerCamelCase					:	int="gelu_fast",			_lowerCamelCase					:	Any=0.0,			_lowerCamelCase					:	Tuple=0.0,			_lowerCamelCase					:	Any=0.02,			_lowerCamelCase					:	List[str]=1e-06,			_lowerCamelCase					:	str=True,			_lowerCamelCase					:	Union[str, Any]=None,			):

     '''simple docstring'''
     __A		     =					parent
     __A		     =					batch_size
     __A		     =					is_training
     __A		     =					use_labels
     __A		     =					num_labels
     __A		     =					image_size
     __A		     =					num_frames
     __A		     =					tubelet_size
     __A		     =					num_channels
     __A		     =					hidden_size
     __A		     =					num_hidden_layers
     __A		     =					num_attention_heads
     __A		     =					intermediate_size
     __A		     =					hidden_act
     __A		     =					hidden_dropout_prob
     __A		     =					attention_probs_dropout_prob
     __A		     =					initializer_range
     __A		     =					layer_norm_eps
     __A		     =					qkv_bias
     __A		     =					scope

     __A		     =					(
         (self.image_size // self.tubelet_size[2])
         * (self.image_size // self.tubelet_size[1])
         * (self.num_frames // self.tubelet_size[0])
     ) + 1  # CLS token


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

     __A		     =					self.get_config()

     return config, pixel_values, labels


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					VivitConfig(
         num_frames=self.num_frames,			image_size=self.image_size,			tubelet_size=self.tubelet_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,			initializer_range=self.initializer_range,			layer_norm_eps=self.layer_norm_eps,			qkv_bias=self.qkv_bias,			)
     __A		     =					self.num_labels
     return config


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str    ):

     '''simple docstring'''
     __A		     =					VivitModel(config=_lowerCamelCase    )
     model.to(_lowerCamelCase    )
     model.eval()
     __A		     =					model(_lowerCamelCase    )
     self.parent.assertEqual(result.last_hidden_state.shape,			(self.batch_size, self.seq_length, self.hidden_size)    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	int,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					VivitForVideoClassification(_lowerCamelCase    )
     model.to(_lowerCamelCase    )
     model.eval()

     __A		     =					model(_lowerCamelCase    )

     # verify the logits shape
     __A		     =					torch.Size((self.batch_size, self.num_labels)    )
     self.parent.assertEqual(result.logits.shape,			_lowerCamelCase    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	str    ):

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





@require_torch
class    snake_case						(				_lowerCAmelCase  ,      unittest.TestCase		):


   '''simple docstring'''

   A_		:      Dict							=					(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

   A_		:      int							=					False
   A_		:      Union[str, Any]							=					False
   A_		:      Any							=					False
   A_		:      Union[str, Any]							=					False


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any]    ):

     '''simple docstring'''
     __A		     =					VivitModelTester(self    )
     __A		     =					ConfigTester(self,			config_class=_lowerCamelCase,			has_text_modality=_lowerCamelCase,			hidden_size=37    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	int,			_lowerCamelCase					:	int=False    ):

     '''simple docstring'''
     __A		     =					copy.deepcopy(_lowerCamelCase    )

     if return_labels:
       if model_class in get_values(_lowerCamelCase    ):
         __A		     =					torch.zeros(
             self.model_tester.batch_size,			dtype=torch.long,			device=_lowerCamelCase    )

     return inputs_dict


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     self.config_tester.run_common_tests()


   @unittest.skip(reason='''Vivit does not use inputs_embeds'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     pass


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A     ,       __A		     =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A		     =					model_class(_lowerCamelCase    )
       self.assertIsInstance(model.get_input_embeddings(),			(nn.Module)    )
       __A		     =					model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(_lowerCamelCase,			nn.Linear    )    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A     ,       __A		     =					self.model_tester.prepare_config_and_inputs_for_common()

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

       __A		     =					['''pixel_values''', '''head_mask''']
       self.assertListEqual(arg_names[:2],			_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*_lowerCamelCase    )


   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __A		     =					VivitModel.from_pretrained(_lowerCamelCase    )
       self.assertIsNotNone(_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A     ,       __A		     =					self.model_tester.prepare_config_and_inputs_for_common()
     __A		     =					True

     for model_class in self.all_model_classes:
       __A		     =					self.model_tester.seq_length

       __A		     =					True
       __A		     =					False
       __A		     =					True
       __A		     =					model_class(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       with torch.no_grad():
         __A		     =					model(**self._prepare_for_class(_lowerCamelCase,			_lowerCamelCase    )    )
       __A		     =					outputs.attentions
       self.assertEqual(len(_lowerCamelCase    ),			self.model_tester.num_hidden_layers    )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __A		     =					True
       __A		     =					model_class(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       with torch.no_grad():
         __A		     =					model(**self._prepare_for_class(_lowerCamelCase,			_lowerCamelCase    )    )
       __A		     =					outputs.attentions
       self.assertEqual(len(_lowerCamelCase    ),			self.model_tester.num_hidden_layers    )

       self.assertListEqual(
           list(attentions[0].shape[-3:]    ),			[self.model_tester.num_attention_heads, seq_len, seq_len],			)
       __A		     =					len(_lowerCamelCase    )

       # Check attention is always last and order is fine
       __A		     =					True
       __A		     =					True
       __A		     =					model_class(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()
       with torch.no_grad():
         __A		     =					model(**self._prepare_for_class(_lowerCamelCase,			_lowerCamelCase    )    )

       self.assertEqual(out_len + 1,			len(_lowerCamelCase    )    )

       __A		     =					outputs.attentions

       self.assertEqual(len(_lowerCamelCase    ),			self.model_tester.num_hidden_layers    )

       self.assertListEqual(
           list(self_attentions[0].shape[-3:]    ),			[self.model_tester.num_attention_heads, seq_len, seq_len],			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     def check_hidden_states_output(_lowerCamelCase					:	str,			_lowerCamelCase					:	int,			_lowerCamelCase					:	int    ):
       __A		     =					model_class(_lowerCamelCase    )
       model.to(_lowerCamelCase    )
       model.eval()

       with torch.no_grad():
         __A		     =					model(**self._prepare_for_class(_lowerCamelCase,			_lowerCamelCase    )    )

       __A		     =					outputs.hidden_states
       __A		     =					self.model_tester.num_hidden_layers + 1
       self.assertEqual(len(_lowerCamelCase    ),			_lowerCamelCase    )

       __A		     =					self.model_tester.seq_length

       self.assertListEqual(
           list(hidden_states[0].shape[-2:]    ),			[seq_length, self.model_tester.hidden_size],			)

     __A     ,       __A		     =					self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       __A		     =					True
       check_hidden_states_output(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )

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

       check_hidden_states_output(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )






   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     pass





def    lowerCAmelCase							(       ):




  """simple docstring"""







  __A		     =					hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''						,			filename='''eating_spaghetti_32_frames.npy'''						,			repo_type='''dataset'''					)
  __A		     =					np.load(__UpperCamelCase					)
  return list(__UpperCamelCase					)





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


   '''simple docstring'''

   @cached_property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     return VivitImageProcessor() if is_vision_available() else None






   @slow
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     __A		     =					VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''    ).to(_lowerCamelCase    )

     __A		     =					self.default_image_processor
     __A		     =					prepare_video()
     __A		     =					image_processor(_lowerCamelCase,			return_tensors='''pt'''    ).to(_lowerCamelCase    )

     # forward pass
     with torch.no_grad():
       __A		     =					model(**_lowerCamelCase    )

     # verify the logits
     __A		     =					torch.Size((1, 4_00)    )
     self.assertEqual(outputs.logits.shape,			_lowerCamelCase    )

     # taken from original model
     __A		     =					torch.tensor([-1.05_43, 2.07_64, -0.21_04, 0.44_39, -0.96_58]    ).to(_lowerCamelCase    )

     self.assertTrue(torch.allclose(outputs.logits[0, :5],			_lowerCamelCase,			atol=1e-4    )    )











'''simple docstring'''


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase					:						List[str]					     =							logging.getLogger(__name__)



class 				__UpperCAmelCase	:







		'''simple docstring'''







		def __init__(self    :   Any   ):
			A					=   False







		def 							A  (self    :   Tuple   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Any   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   Dict   ):
			if not self.initialized:
				A					=   RagRetriever(
				    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )
				A					=   True







		def 							A  (self    :   List[str]   ):
			self.retriever.index.init_index()







		def 							A  (self    :   List[str]   ):
			# delete the old self.retriever object before assigning the new index
			del self.retriever
			A					=   False







		def 							A  (self    :   Any   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   Optional[int]   ):
			A	,     A					=   self.retriever._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.retriever.index.get_doc_dicts(_lowerCAmelCase   )
			return doc_ids, retrieved_doc_embeds, doc_dicts






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def __init__(self    :   Any   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]=None   ):
			if index is not None and index.is_initialized() and len(_lowerCAmelCase   ) > 0:
				raise ValueError(
				    """When using Ray for distributed fine-tuning, """
				    """you'll need to provide the paths instead, """
				    """as the dataset and the index are loaded """
				    """separately. More info in examples/rag/use_own_knowledge_dataset.py """   )

			super().__init__(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   init_retrieval=_lowerCAmelCase   ,   )

			A					=   retrieval_workers
			A					=   question_encoder_tokenizer
			A					=   generator_tokenizer
			if len(self.retrieval_workers   ) > 0:
				ray.get(
				    [
				        worker.create_rag_retriever.remote(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
				        for worker in self.retrieval_workers
				    ]   )







		def 							A  (self    :   Optional[Any]   ):
			logger.info("""initializing retrieval"""   )

			if len(self.retrieval_workers   ) > 0:
				ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]   )
			else:
				# Non-distributed training. Load index into this same process.
				self.index.init_index()







		def 							A  (self    :   int   ,   _lowerCAmelCase    :   Dict   ,   _lowerCAmelCase    :   List[Any]   ):
			if len(self.retrieval_workers   ) > 0:
				# Select a random retrieval actor.
				A					=   self.retrieval_workers[random.randint(0   ,   len(self.retrieval_workers   ) - 1   )]
				A	,     A	,     A					=   ray.get(
				    random_worker.retrieve.remote(_lowerCAmelCase   ,   _lowerCAmelCase   )   )
			else:
				A	,     A					=   self._main_retrieve(_lowerCAmelCase   ,   _lowerCAmelCase   )
				A					=   self.index.get_doc_dicts(_lowerCAmelCase   )
			return retrieved_doc_embeds, doc_ids, doc_dicts







		@classmethod
		def 							A  (cls    :   Dict   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   str=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			return super(_lowerCAmelCase   ,   cls   ).get_tokenizers(_lowerCAmelCase   ,   _lowerCAmelCase   ,   **_lowerCAmelCase   )







		@classmethod
		def 							A  (cls    :   List[str]   ,   _lowerCAmelCase    :   List[str]   ,   _lowerCAmelCase    :   List[Any]   ,   _lowerCAmelCase    :   Optional[int]=None   ,   **_lowerCAmelCase    :   Optional[int]   ):
			A					=   kwargs.pop("""config"""   ,   _lowerCAmelCase   ) or RagConfig.from_pretrained(_lowerCAmelCase   ,   **_lowerCAmelCase   )
			A					=   RagTokenizer.from_pretrained(_lowerCAmelCase   ,   config=_lowerCAmelCase   )
			A					=   rag_tokenizer.question_encoder
			A					=   rag_tokenizer.generator

			if indexed_dataset is not None:
				A					=   """custom"""
				A					=   CustomHFIndex(config.retrieval_vector_size   ,   _lowerCAmelCase   )
			else:
				A					=   cls._build_index(_lowerCAmelCase   )

			return cls(
			    _lowerCAmelCase   ,   question_encoder_tokenizer=_lowerCAmelCase   ,   generator_tokenizer=_lowerCAmelCase   ,   retrieval_workers=_lowerCAmelCase   ,   index=_lowerCAmelCase   ,   )







		def 							A  (self    :   str   ):
			logger.info("""re-loading the new dataset with embeddings"""   )
			# access from the training loop

			ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]   )

			# build the index object again
			A					=   self._build_index(self.config   )

			ray.get(
			    [
			        worker.create_rag_retriever.remote(
			            self.config   ,   self.question_encoder_tokenizer   ,   self.generator_tokenizer   ,   _lowerCAmelCase   )
			        for worker in self.retrieval_workers
			    ]   )

