



'''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 							a__:







    def __init__(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  str=13						,      __snake_case			:  Dict=10						,      __snake_case			:  Any=3						,      __snake_case			:  Union[str, Any]=2						,      __snake_case			:  Optional[int]=2						,      __snake_case			:  Union[str, Any]=True						,      __snake_case			:  Any=True						,      __snake_case			:  Union[str, Any]=32						,      __snake_case			:  Optional[int]=5						,      __snake_case			:  Union[str, Any]=4						,      __snake_case			:  Tuple=37						,      __snake_case			:  List[str]="gelu"						,      __snake_case			:  List[str]=0.1						,      __snake_case			:  str=0.1						,      __snake_case			:  List[Any]=10						,      __snake_case			:  Optional[int]=0.02						,      __snake_case			:  str="divided_space_time"						,      __snake_case			:  int=None						,      ):
      a						: Any					       =					parent
      a						: Optional[Any]					       =					batch_size
      a						: str					       =					image_size
      a						: List[str]					       =					num_channels
      a						: List[Any]					       =					patch_size
      a						: Union[str, Any]					       =					num_frames
      a						: List[str]					       =					is_training
      a						: int					       =					use_labels
      a						: Union[str, Any]					       =					hidden_size
      a						: str					       =					num_hidden_layers
      a						: List[str]					       =					num_attention_heads
      a						: str					       =					intermediate_size
      a						: Dict					       =					hidden_act
      a						: List[Any]					       =					hidden_dropout_prob
      a						: str					       =					attention_probs_dropout_prob
      a						: List[Any]					       =					attention_type
      a						: Union[str, Any]					       =					initializer_range
      a						: Optional[Any]					       =					scope
      a						: Any					       =					num_labels

      # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
      a						: Optional[Any]					       =					(image_size // patch_size) ** 2
      a						: List[Any]					       =					(num_frames) * self.num_patches_per_frame + 1







    def       lowercase_	(				self			:  Tuple     ):
      a						: Union[str, Any]					       =					floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

      a						: Any					       =					None
      if self.use_labels:
        a						: List[Any]					       =					ids_tensor([self.batch_size]						,      self.num_labels     )

      a						: Optional[Any]					       =					self.get_config()

      return config, pixel_values, labels







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Any					       =					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						: List[str]					       =					self.num_labels
      return config







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Union[str, Any]     ):
      a						: Optional[Any]					       =					TimesformerModel(config=__snake_case     )
      model.to(__snake_case     )
      model.eval()
      a						: Optional[Any]					       =					model(__snake_case     )
      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)     )







    def       lowercase_	(				self			:  Tuple						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  Tuple     ):
      a						: Dict					       =					TimesformerForVideoClassification(__snake_case     )
      model.to(__snake_case     )
      model.eval()

      a						: Tuple					       =					model(__snake_case     )

      # verify the logits shape
      a						: Any					       =					torch.Size((self.batch_size, self.num_labels)     )
      self.parent.assertEqual(result.logits.shape						,      __snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: List[Any]					       =					self.prepare_config_and_inputs()
      a       ,     a       ,     a						: Dict					       =					config_and_inputs
      a						: Optional[int]					       =					{'pixel_values': pixel_values}
      return config, inputs_dict

@require_torch
class 							a__( lowerCamelCase__		,			lowerCamelCase__		,			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       lowercase_	(				self			:  List[str]     ):
      a						: Any					       =					TimesformerModelTester(self     )
      a						: Union[str, Any]					       =					ConfigTester(
          self						,      config_class=__snake_case						,      has_text_modality=__snake_case						,      hidden_size=37     )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  Any						,      __snake_case			:  Any						,      __snake_case			:  int=False     ):
      a						: Optional[Any]					       =					copy.deepcopy(__snake_case     )

      if return_labels:
        if model_class in get_values(__snake_case     ):
          a						: Any					       =					torch.zeros(
              self.model_tester.batch_size						,      dtype=torch.long						,      device=__snake_case     )

      return inputs_dict







    def       lowercase_	(				self			:  Any     ):
      self.config_tester.run_common_tests()







    @unittest.skip(reason='TimeSformer does not use inputs_embeds'     )
    def       lowercase_	(				self			:  Any     ):
      pass







    def       lowercase_	(				self			:  Any     ):
      a       ,     a						: str					       =					self.model_tester.prepare_config_and_inputs_for_common()

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







    def       lowercase_	(				self			:  Tuple     ):
      a       ,     a						: Dict					       =					self.model_tester.prepare_config_and_inputs_for_common()

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

        a						: List[str]					       =					['pixel_values']
        self.assertListEqual(arg_names[:1]						,      __snake_case     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Union[str, Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__snake_case     )







    def       lowercase_	(				self			:  int     ):
      a						: List[str]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*__snake_case     )







    @slow
    def       lowercase_	(				self			:  int     ):
      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: List[Any]					       =					TimesformerModel.from_pretrained(__snake_case     )
        self.assertIsNotNone(__snake_case     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      if not self.has_attentions:
        pass

      else:
        a       ,     a						: Any					       =					self.model_tester.prepare_config_and_inputs_for_common()
        a						: List[Any]					       =					True

        for model_class in self.all_model_classes:
          a						: str					       =					self.model_tester.seq_length
          a						: int					       =					self.model_tester.num_frames

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

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

          # Check attention is always last and order is fine
          a						: Optional[Any]					       =					True
          a						: Optional[Any]					       =					True
          a						: List[Any]					       =					model_class(__snake_case     )
          model.to(__snake_case     )
          model.eval()
          with torch.no_grad():
            a						: List[Any]					       =					model(**self._prepare_for_class(__snake_case						,      __snake_case     )     )

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

          a						: Dict					       =					outputs.attentions

          self.assertEqual(len(__snake_case     )						,      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       lowercase_	(				self			:  str     ):
      def check_hidden_states_output(__snake_case			:  Optional[Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  int     ):
        a						: List[Any]					       =					model_class(__snake_case     )
        model.to(__snake_case     )
        model.eval()

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

        a						: Any					       =					outputs.hidden_states
        a						: List[str]					       =					self.model_tester.num_hidden_layers + 1
        self.assertEqual(len(__snake_case     )						,      __snake_case     )

        a						: Any					       =					self.model_tester.seq_length

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

      a       ,     a						: List[Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Tuple					       =					True
        check_hidden_states_output(__snake_case						,      __snake_case						,      __snake_case     )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        a						: Tuple					       =					True

        check_hidden_states_output(__snake_case						,      __snake_case						,      __snake_case     )
def 				lowerCamelCase__	(						):
  a						: Optional[int]					       =					hf_hub_download(
      repo_id='hf-internal-testing/spaghetti-video'			,      filename='eating_spaghetti.npy'			,      repo_type='dataset'    )
  a						: str					       =					np.load(_A    )
  return list(_A    )

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







    @cached_property
    def       lowercase_	(				self			:  str     ):
      # 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       lowercase_	(				self			:  Optional[Any]     ):
      a						: Tuple					       =					TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'     ).to(
          __snake_case     )

      a						: Tuple					       =					self.default_image_processor
      a						: Union[str, Any]					       =					prepare_video()
      a						: Optional[int]					       =					image_processor(video[:8]						,      return_tensors='pt'     ).to(__snake_case     )

      # forward pass
      with torch.no_grad():
        a						: str					       =					model(**__snake_case     )

      # verify the logits
      a						: Dict					       =					torch.Size((1, 4_00)     )
      self.assertEqual(outputs.logits.shape						,      __snake_case     )

      a						: int					       =					torch.tensor([-0.3016, -0.7713, -0.4205]     ).to(__snake_case     )

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




'''simple docstring'''






import torch

from transformers import AutoModel

class 							a__( torch.nn.Module     ):







    def __init__(				self			:  Any						,      __snake_case			:  str="sayef/fsner-bert-base-uncased"     ):
      super(__snake_case						,      self     ).__init__()

      a						: List[str]					       =					AutoModel.from_pretrained(__snake_case						,      return_dict=__snake_case     )
      a						: Optional[Any]					       =					torch.nn.CosineSimilarity(3						,      1e-0_8     )
      a						: Tuple					       =					torch.nn.Softmax(dim=1     )







    def       lowercase_	(				self			:  List[str]						,      **__snake_case			:  int     ):
      return self.bert(**__snake_case     ).last_hidden_state







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]     ):
      return token_embeddings.sum(2						,      keepdim=__snake_case     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Tuple						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[Any]=1     ):
      return self.softmax(T * self.cos(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  int						,      __snake_case			:  int						,      __snake_case			:  Tuple     ):
      a						: List[Any]					       =					W_supports['sizes'].tolist()
      a						: Any					       =					W_supports['start_token_id'].item()
      a						: int					       =					W_supports['end_token_id'].item()

      del W_supports["sizes"]
      del W_supports["start_token_id"]
      del W_supports["end_token_id"]

      a						: Optional[Any]					       =					self.BERT(**__snake_case     )
      a						: Tuple					       =					self.BERT(**__snake_case     )

      a						: Dict					       =					None
      a						: Optional[Any]					       =					None

      a						: Union[str, Any]					       =					W_supports['input_ids'] == start_token_id
      a						: str					       =					W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__snake_case     ):
        if i == 0:
          a						: Optional[int]					       =					0
        else:
          a						: Tuple					       =					support_sizes[i - 1]

        a						: Tuple					       =					S[s : s + size][start_token_masks[s : s + size]]
        a						: int					       =					S[s : s + size][end_token_masks[s : s + size]]

        a						: Any					       =					torch.matmul(q[i]						,      s_start.T     ).sum(1     ).softmax(0     )
        a						: List[str]					       =					torch.matmul(q[i]						,      s_end.T     ).sum(1     ).softmax(0     )

        if p_starts is not None:
          a						: List[Any]					       =					torch.vstack((p_starts, p_start)     )
          a						: List[Any]					       =					torch.vstack((p_ends, p_end)     )
        else:
          a						: List[Any]					       =					p_start
          a						: Optional[int]					       =					p_end

      return p_starts, p_ends