
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 							A__		:
      def __init__(				self   ,  __magic_name__   ,  __magic_name__=2   ,  __magic_name__=True   ,  __magic_name__=True   ,  __magic_name__=1_0   ,  __magic_name__=1_0   ,  __magic_name__=8   ,  __magic_name__=[2, 4, 4]   ,  __magic_name__=3   ,  __magic_name__=7_6_8   ,  __magic_name__=5   ,  __magic_name__=4   ,  __magic_name__=3_7   ,  __magic_name__="gelu_fast"   ,  __magic_name__=0.0   ,  __magic_name__=0.0   ,  __magic_name__=0.02   ,  __magic_name__=1e-06   ,  __magic_name__=True   ,  __magic_name__=None   ,  ):
       lowerCamelCase		:     List[str]          =				parent
       lowerCamelCase		:     Union[str, Any]          =				batch_size
       lowerCamelCase		:     Tuple          =				is_training
       lowerCamelCase		:     Optional[Any]          =				use_labels
       lowerCamelCase		:     int          =				num_labels
       lowerCamelCase		:     Union[str, Any]          =				image_size
       lowerCamelCase		:     List[str]          =				num_frames
       lowerCamelCase		:     Any          =				tubelet_size
       lowerCamelCase		:     str          =				num_channels
       lowerCamelCase		:     List[Any]          =				hidden_size
       lowerCamelCase		:     List[str]          =				num_hidden_layers
       lowerCamelCase		:     Union[str, Any]          =				num_attention_heads
       lowerCamelCase		:     Optional[Any]          =				intermediate_size
       lowerCamelCase		:     Tuple          =				hidden_act
       lowerCamelCase		:     str          =				hidden_dropout_prob
       lowerCamelCase		:     Union[str, Any]          =				attention_probs_dropout_prob
       lowerCamelCase		:     int          =				initializer_range
       lowerCamelCase		:     Dict          =				layer_norm_eps
       lowerCamelCase		:     Optional[Any]          =				qkv_bias
       lowerCamelCase		:     int          =				scope

       lowerCamelCase		:     str          =				(
           (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 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)

       lowerCamelCase		:     str          =				None
       if self.use_labels:
        lowerCamelCase		:     Union[str, Any]          =				ids_tensor([self.batch_size]   ,  self.num_labels				)

       lowerCamelCase		:     str          =				self.get_config()

       return config, pixel_values, labels
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[Any]          =				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   ,  )
       lowerCamelCase		:     Optional[Any]          =				self.num_labels
       return config
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Any          =				VivitModel(config=__magic_name__				)
       model.to(__magic_name__				)
       model.eval()
       lowerCamelCase		:     List[Any]          =				model(__magic_name__				)
       self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     List[str]          =				VivitForVideoClassification(__magic_name__				)
       model.to(__magic_name__				)
       model.eval()

       lowerCamelCase		:     Optional[int]          =				model(__magic_name__				)

       # verify the logits shape
       lowerCamelCase		:     List[Any]          =				torch.Size((self.batch_size, self.num_labels)				)
       self.parent.assertEqual(result.logits.shape   ,  __magic_name__				)






      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				self.prepare_config_and_inputs()
       lowerCamelCase		:     int          =				config_and_inputs
       lowerCamelCase		:     Tuple          =				{"pixel_values": pixel_values}
       return config, inputs_dict





@require_torch
class 							A__		(     lowerCamelCase_  ,       unittest.TestCase):
      _UpperCAmelCase    :   List[str]   	=      (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

      _UpperCAmelCase    :   Any   	=      False
      _UpperCAmelCase    :   Any   	=      False
      _UpperCAmelCase    :   Optional[int]   	=      False
      _UpperCAmelCase    :   Optional[int]   	=      False
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				VivitModelTester(self				)
       lowerCamelCase		:     Tuple          =				ConfigTester(self   ,  config_class=__magic_name__   ,  has_text_modality=__magic_name__   ,  hidden_size=3_7				)
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__=False				):
       lowerCamelCase		:     int          =				copy.deepcopy(__magic_name__				)

       if return_labels:
        if model_class in get_values(__magic_name__				):
         lowerCamelCase		:     str          =				torch.zeros(
             self.model_tester.batch_size   ,  dtype=torch.long   ,  device=__magic_name__				)

       return inputs_dict
      def 						UpperCamelCase__							(				self				):
       self.config_tester.run_common_tests()
      @unittest.skip(reason="""Vivit does not use inputs_embeds"""				)
      def 						UpperCamelCase__							(				self				):
       pass
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Optional[int]          =				model_class(__magic_name__				)
        self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module)				)
        lowerCamelCase		:     Optional[int]          =				model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__magic_name__   ,  nn.Linear				)				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Optional[int]          =				model_class(__magic_name__				)
        lowerCamelCase		:     str          =				inspect.signature(model.forward				)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase		:     Optional[Any]          =				[*signature.parameters.keys()]

        lowerCamelCase		:     Optional[int]          =				["pixel_values", "head_mask"]
        self.assertListEqual(arg_names[:2]   ,  __magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_video_classification(*__magic_name__				)
      @slow
      def 						UpperCamelCase__							(				self				):







       for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:     List[Any]          =				VivitModel.from_pretrained(__magic_name__				)
        self.assertIsNotNone(__magic_name__				)
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Tuple          =				self.model_tester.prepare_config_and_inputs_for_common()
       lowerCamelCase		:     str          =				True

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Tuple          =				self.model_tester.seq_length

        lowerCamelCase		:     Tuple          =				True
        lowerCamelCase		:     Tuple          =				False
        lowerCamelCase		:     str          =				True
        lowerCamelCase		:     str          =				model_class(__magic_name__				)
        model.to(__magic_name__				)
        model.eval()
        with torch.no_grad():
         lowerCamelCase		:     Tuple          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)
        lowerCamelCase		:     List[Any]          =				outputs.attentions
        self.assertEqual(len(__magic_name__				)   ,  self.model_tester.num_hidden_layers				)

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

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

        # Check attention is always last and order is fine
        lowerCamelCase		:     Any          =				True
        lowerCamelCase		:     Tuple          =				True
        lowerCamelCase		:     Optional[int]          =				model_class(__magic_name__				)
        model.to(__magic_name__				)
        model.eval()
        with torch.no_grad():
         lowerCamelCase		:     Union[str, Any]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)

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

        lowerCamelCase		:     List[Any]          =				outputs.attentions

        self.assertEqual(len(__magic_name__				)   ,  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 						UpperCamelCase__							(				self				):







       def check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__				):
        lowerCamelCase		:     Dict          =				model_class(__magic_name__				)
        model.to(__magic_name__				)
        model.eval()

        with torch.no_grad():
         lowerCamelCase		:     List[Any]          =				model(**self._prepare_for_class(__magic_name__   ,  __magic_name__				)				)

        lowerCamelCase		:     List[Any]          =				outputs.hidden_states
        lowerCamelCase		:     Any          =				self.model_tester.num_hidden_layers + 1
        self.assertEqual(len(__magic_name__				)   ,  __magic_name__				)

        lowerCamelCase		:     str          =				self.model_tester.seq_length

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

       lowerCamelCase		:     Any          =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:     Optional[Any]          =				True
        check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__				)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowerCamelCase		:     Union[str, Any]          =				True

        check_hidden_states_output(__magic_name__   ,  __magic_name__   ,  __magic_name__				)






      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
      def 						UpperCamelCase__							(				self				):
       pass





def    _a (						):
 lowerCamelCase		:     Any          =				hf_hub_download(
     repo_id="""hf-internal-testing/spaghetti-video""",     filename="""eating_spaghetti_32_frames.npy""",     repo_type="""dataset"""							)
 lowerCamelCase		:     List[Any]          =				np.load(lowercase__							)
 return list(lowercase__							)





@require_torch
@require_vision
class 							A__		(     unittest.TestCase):
      @cached_property
      def 						UpperCamelCase__							(				self				):
       return VivitImageProcessor() if is_vision_available() else None






      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				VivitForVideoClassification.from_pretrained("""google/vivit-b-16x2-kinetics400"""				).to(__magic_name__				)

       lowerCamelCase		:     Optional[int]          =				self.default_image_processor
       lowerCamelCase		:     List[str]          =				prepare_video()
       lowerCamelCase		:     List[Any]          =				image_processor(__magic_name__   ,  return_tensors="""pt"""				).to(__magic_name__				)

       # forward pass
       with torch.no_grad():
        lowerCamelCase		:     str          =				model(**__magic_name__				)

       # verify the logits
       lowerCamelCase		:     str          =				torch.Size((1, 4_0_0)				)
       self.assertEqual(outputs.logits.shape   ,  __magic_name__				)

       # taken from original model
       lowerCamelCase		:     Optional[Any]          =				torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]				).to(__magic_name__				)

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






def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(sum_of_harmonic_progression(1 / 2, 2, 2))




