





'''simple docstring'''

import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase							:     List[Any]		     ="▁"

__lowerCAmelCase							:     List[Any]		     =get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase     (	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    BigBirdTokenizer
 __lowercase       		=    BigBirdTokenizerFast
 __lowercase       		=    True
 __lowercase       		=    True
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Any:
     super().setUp()

     A__    =      self.tokenizer_class(lowercase_						,	keep_accents=lowercase_							)
     tokenizer.save_pretrained(self.tmpdirname							)
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      "<s>"
     A__    =      1

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_							)						,	lowercase_							)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_							)						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      list(self.get_tokenizer().get_vocab().keys()							)

     self.assertEqual(vocab_keys[0]						,	"<unk>"							)
     self.assertEqual(vocab_keys[1]						,	"<s>"							)
     self.assertEqual(vocab_keys[-1]						,	"[MASK]"							)
     self.assertEqual(len(lowercase_							)						,	10_04							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     self.assertEqual(self.get_tokenizer().vocab_size						,	10_00							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     if not self.test_rust_tokenizer:
         return

     A__    =      self.get_tokenizer()
     A__    =      self.get_rust_tokenizer()

     A__    =      "I was born in 92000, and this is falsé."

     A__    =      tokenizer.tokenize(lowercase_							)
     A__    =      rust_tokenizer.tokenize(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     A__    =      rust_tokenizer.encode(lowercase_						,	add_special_tokens=lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)

     A__    =      self.get_rust_tokenizer()
     A__    =      tokenizer.encode(lowercase_							)
     A__    =      rust_tokenizer.encode(lowercase_							)
     self.assertListEqual(lowercase_						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:int							)->							List[str]:
     A__    =      BigBirdTokenizer(lowercase_						,	keep_accents=lowercase_							)

     A__    =      tokenizer.tokenize("This is a test"							)
     self.assertListEqual(lowercase_						,	["▁This", "▁is", "▁a", "▁t", "est"]							)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(lowercase_							)						,	[2_85, 46, 10, 1_70, 3_82]						,	)

     A__    =      tokenizer.tokenize("I was born in 92000, and this is falsé."							)
     self.assertListEqual(
         lowercase_						,	[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "9",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "é",
             ".",
         ]						,	)
     A__    =      tokenizer.convert_tokens_to_ids(lowercase_							)
     self.assertListEqual(
         lowercase_						,	[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,	)

     A__    =      tokenizer.convert_ids_to_tokens(lowercase_							)
     self.assertListEqual(
         lowercase_						,	[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "<unk>",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "<unk>",
             ".",
         ]						,	)
 @cached_property
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Union[str, Any]:
     return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"							)
 @slow
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Union[str, Any]:
     A__    =      "Hello World!"
     A__    =      [65, 1_85_36, 22_60, 1_01, 66]

     self.assertListEqual(lowercase_						,	self.big_tokenizer.encode(lowercase_							)							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     A__    =      (
         "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
         " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
     )
     # fmt: off
     A__    =      [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
     # fmt: on
     self.assertListEqual(lowercase_						,	self.big_tokenizer.encode(lowercase_							)							)
 @require_torch
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							int:
     import torch

     from transformers import BigBirdConfig, BigBirdModel

     # Build sequence
     A__    =      list(self.big_tokenizer.get_vocab().keys()							)[:10]
     A__    =      " ".join(lowercase_							)
     A__    =      self.big_tokenizer.encode_plus(lowercase_						,	return_tensors="pt"						,	return_token_type_ids=lowercase_							)
     A__    =      self.big_tokenizer.batch_encode_plus(
         [sequence + " " + sequence]						,	return_tensors="pt"						,	return_token_type_ids=lowercase_							)

     A__    =      BigBirdConfig(attention_type="original_full"							)
     A__    =      BigBirdModel(lowercase_							)

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
         model(**lowercase_							)
         model(**lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     A__    =      BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"							)
     A__    =      tokenizer.decode(tokenizer("Paris is the [MASK]."							).input_ids							)

     self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Dict:
     # fmt: off
     A__    =      {"input_ids": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=lowercase_						,	model_name="google/bigbird-roberta-base"						,	revision="215c99f1600e06f83acce68422f2035b2b5c3510"						,	)










'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class        UpperCAmelCase     :
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Union[str, Any]=10						,	lowercase_				:Any=3						,	lowercase_				:Tuple=2						,	lowercase_				:List[Any]=2						,	lowercase_				:int=True						,	lowercase_				:int=True						,	lowercase_				:List[str]=32						,	lowercase_				:Dict=5						,	lowercase_				:List[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:List[Any]="gelu"						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:List[Any]=10						,	lowercase_				:int=0.0_2						,	lowercase_				:Union[str, Any]="divided_space_time"						,	lowercase_				:Tuple=None						,	)->							Tuple:
     A__    =      parent
     A__    =      batch_size
     A__    =      image_size
     A__    =      num_channels
     A__    =      patch_size
     A__    =      num_frames
     A__    =      is_training
     A__    =      use_labels
     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__    =      attention_type
     A__    =      initializer_range
     A__    =      scope
     A__    =      num_labels

     # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
     A__    =      (image_size // patch_size) ** 2
     A__    =      (num_frames) * self.num_patches_per_frame + 1
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     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 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerConfig(
         image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_frames=self.num_frames						,	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						,	attention_type=self.attention_type						,	)
     A__    =      self.num_labels
     return config
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Optional[int]:
     A__    =      TimesformerModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Dict							)->							Tuple:
     A__    =      TimesformerForVideoClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()

     A__    =      model(lowercase_							)

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



 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__    =      config_and_inputs
     A__    =      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 __lowercase       		=    (
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      TimesformerModelTester(self							)
     A__    =      ConfigTester(
         self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Dict						,	lowercase_				:int=False							)->							str:
     A__    =      copy.deepcopy(lowercase_							)

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

     return inputs_dict
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="TimeSformer does not use inputs_embeds"							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)							)
         A__    =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_						,	nn.Linear							)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

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

         A__    =      ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      TimesformerModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if not self.has_attentions:
         pass

     else:
         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__    =      self.model_tester.num_frames

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

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

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)
             A__    =      len(lowercase_							)

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

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

             A__    =      outputs.attentions

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

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     def check_hidden_states_output(lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							):
         A__    =      model_class(lowercase_							)
         model.to(lowercase_							)
         model.eval()

         with torch.no_grad():
             A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

         A__    =      outputs.hidden_states
         A__    =      self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowercase_							)						,	lowercase_							)

         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(lowercase_						,	lowercase_						,	lowercase_							)

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

         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)





def 						UpperCamelCase    (							):
    A__    =      hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,	filename="eating_spaghetti.npy"						,	repo_type="dataset"	)
    A__    =      np.load(_lowerCamelCase	)
    return list(_lowerCamelCase	)






@require_torch
@require_vision
class        UpperCAmelCase     (	unittest.TestCase    ):
 @cached_property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     # logits were tested with a different mean and std, so we use the same here
     return (
         VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,	image_std=[0.5, 0.5, 0.5]							)
         if is_vision_available()
         else None
     )



 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"							).to(
         lowercase_							)

     A__    =      self.default_image_processor
     A__    =      prepare_video()
     A__    =      image_processor(video[:8]						,	return_tensors="pt"							).to(lowercase_							)

     # forward pass
     with torch.no_grad():
         A__    =      model(**lowercase_							)

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

     A__    =      torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]							).to(lowercase_							)

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



