






"""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 copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase      :							List[str]          =					logging.get_logger(__name__)

UpperCamelCase      :							Dict          =					{
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):
     lowercase		  =		"oneformer"
     lowercase		  =		{"hidden_size": "hidden_dim"}





     def __init__(      self      ,	__UpperCAmelCase = None      ,	__UpperCAmelCase = 255      ,	__UpperCAmelCase = 150      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 5.0      ,	__UpperCAmelCase = 0.5      ,	__UpperCAmelCase = 0.0_7      ,	__UpperCAmelCase = 1_2544      ,	__UpperCAmelCase = 3.0      ,	__UpperCAmelCase = 0.7_5      ,	__UpperCAmelCase = 0.0_2      ,	__UpperCAmelCase = 1.0      ,	__UpperCAmelCase = 1E-05      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = [4, 8, 16, 32]      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 77      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 4_9408      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 16      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 256      ,	__UpperCAmelCase = 1024      ,	__UpperCAmelCase = "GN"      ,	__UpperCAmelCase = 6      ,	__UpperCAmelCase = 10      ,	__UpperCAmelCase = True      ,	__UpperCAmelCase = 8      ,	__UpperCAmelCase = 0.1      ,	__UpperCAmelCase = 2048      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = False      ,	__UpperCAmelCase = 2      ,	__UpperCAmelCase = 4      ,	**__UpperCAmelCase      ,	):

      '''simple docstring'''




      if backbone_config is None:
       logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.' )
       __UpperCamelCase							    =			CONFIG_MAPPING['swin'](
           image_size=224      ,	in_channels=3      ,	patch_size=4      ,	embed_dim=96      ,	depths=[2, 2, 6, 2]      ,	num_heads=[3, 6, 12, 24]      ,	window_size=7      ,	drop_path_rate=0.3      ,	use_absolute_embeddings=__UpperCAmelCase      ,	out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,	)
      elif isinstance(__UpperCAmelCase      ,	__UpperCAmelCase ):
       __UpperCamelCase							    =			backbone_config.get('model_type' )
       __UpperCamelCase							    =			CONFIG_MAPPING[backbone_model_type]
       __UpperCamelCase							    =			config_class.from_dict(__UpperCAmelCase )

      __UpperCamelCase							    =			backbone_config

      __UpperCamelCase							    =			ignore_value
      __UpperCamelCase							    =			num_queries
      __UpperCamelCase							    =			no_object_weight
      __UpperCamelCase							    =			class_weight
      __UpperCamelCase							    =			mask_weight
      __UpperCamelCase							    =			dice_weight
      __UpperCamelCase							    =			contrastive_weight
      __UpperCamelCase							    =			contrastive_temperature
      __UpperCamelCase							    =			train_num_points
      __UpperCamelCase							    =			oversample_ratio
      __UpperCamelCase							    =			importance_sample_ratio
      __UpperCamelCase							    =			init_std
      __UpperCamelCase							    =			init_xavier_std
      __UpperCamelCase							    =			layer_norm_eps
      __UpperCamelCase							    =			is_training
      __UpperCamelCase							    =			use_auxiliary_loss
      __UpperCamelCase							    =			output_auxiliary_logits
      __UpperCamelCase							    =			strides
      __UpperCamelCase							    =			task_seq_len
      __UpperCamelCase							    =			text_encoder_width
      __UpperCamelCase							    =			text_encoder_context_length
      __UpperCamelCase							    =			text_encoder_num_layers
      __UpperCamelCase							    =			text_encoder_vocab_size
      __UpperCamelCase							    =			text_encoder_proj_layers
      __UpperCamelCase							    =			text_encoder_n_ctx
      __UpperCamelCase							    =			conv_dim
      __UpperCamelCase							    =			mask_dim
      __UpperCamelCase							    =			hidden_dim
      __UpperCamelCase							    =			encoder_feedforward_dim
      __UpperCamelCase							    =			norm
      __UpperCamelCase							    =			encoder_layers
      __UpperCamelCase							    =			decoder_layers
      __UpperCamelCase							    =			use_task_norm
      __UpperCamelCase							    =			num_attention_heads
      __UpperCamelCase							    =			dropout
      __UpperCamelCase							    =			dim_feedforward
      __UpperCamelCase							    =			pre_norm
      __UpperCamelCase							    =			enforce_input_proj
      __UpperCamelCase							    =			query_dec_layers
      __UpperCamelCase							    =			common_stride
      __UpperCamelCase							    =			decoder_layers

      super().__init__(**__UpperCAmelCase )







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			copy.deepcopy(self.__dict__ )
      __UpperCamelCase							    =			self.backbone_config.to_dict()
      __UpperCamelCase							    =			self.__class__.model_type
      return output



