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 :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=13     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=10     ,							__SCREAMING_SNAKE_CASE :      List[str]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=True     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=32     ,							__SCREAMING_SNAKE_CASE :      str=5     ,							__SCREAMING_SNAKE_CASE :      int=4     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=37     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Tuple=10     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="divided_space_time"     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=None     ,							):
   __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 :      Dict       ):
   __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 :      str       ):
   __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 :      str     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   __a       =  TimesformerModel(config=UpperCAmelCase__       )
   model.to(UpperCAmelCase__       )
   model.eval()
   __a       =  model(UpperCAmelCase__       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  TimesformerForVideoClassification(UpperCAmelCase__       )
   model.to(UpperCAmelCase__       )
   model.eval()

   __a       =  model(UpperCAmelCase__       )

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

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.prepare_config_and_inputs()
   __a       =  config_and_inputs
   __a       =  {'''pixel_values''': pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       lowercase__			,       lowercase__			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			(
      {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  TimesformerModelTester(self       )
   __a       =  ConfigTester(
       self     ,							config_class=UpperCAmelCase__     ,							has_text_modality=UpperCAmelCase__     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict=False       ):
   __a       =  copy.deepcopy(UpperCAmelCase__       )

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

   return inputs_dict

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

  @unittest.skip(reason="TimeSformer does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   pass

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  model_class(UpperCAmelCase__       )
    __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]     ,							UpperCAmelCase__       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase__       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_video_classification(*UpperCAmelCase__       )

  @slow
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  TimesformerModel.from_pretrained(UpperCAmelCase__       )
    self.assertIsNotNone(UpperCAmelCase__       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   if not self.has_attentions:
    pass

   else:
    __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(UpperCAmelCase__       )
     model.to(UpperCAmelCase__       )
     model.eval()
     with torch.no_grad():
      __a       =  model(**self._prepare_for_class(UpperCAmelCase__     ,							UpperCAmelCase__       )       )
     __a       =  outputs.attentions
     self.assertEqual(len(UpperCAmelCase__       )     ,							self.model_tester.num_hidden_layers       )

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

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

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

     __a       =  outputs.attentions

     self.assertEqual(len(UpperCAmelCase__       )     ,							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 :      Dict       ):
   def check_hidden_states_output(__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
    __a       =  model_class(UpperCAmelCase__       )
    model.to(UpperCAmelCase__       )
    model.eval()

    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(UpperCAmelCase__     ,							UpperCAmelCase__       )       )

    __a       =  outputs.hidden_states
    __a       =  self.model_tester.num_hidden_layers + 1
    self.assertEqual(len(UpperCAmelCase__       )     ,							UpperCAmelCase__       )

    __a       =  self.model_tester.seq_length

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

   __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  True
    check_hidden_states_output(UpperCAmelCase__     ,							UpperCAmelCase__     ,							UpperCAmelCase__       )

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

    check_hidden_states_output(UpperCAmelCase__     ,							UpperCAmelCase__     ,							UpperCAmelCase__       )



def   __A		(			):



 """simple docstring"""


 __a       =  hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"							,				filename="eating_spaghetti.npy"							,				repo_type="dataset"					)
 __a       =  np.load(_A					)
 return list(_A					)






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

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   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 :      Any       ):
   __a       =  TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"       ).to(
       UpperCAmelCase__       )

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

   # forward pass
   with torch.no_grad():
    __a       =  model(**UpperCAmelCase__       )

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

   __a       =  torch.tensor([-0.30_16, -0.77_13, -0.42_05]       ).to(UpperCAmelCase__       )

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





from datetime import datetime as dt
import os

from github import Github


SCREAMING_SNAKE_CASE :	Optional[Any]							     =		[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """feature request""",
    """new model""",
    """wip""",
]



def   __A		(			):



 """simple docstring"""


 __a       =  Github(os.environ["GITHUB_TOKEN"]					)
 __a       =  g.get_repo("huggingface/transformers"					)
 __a       =  repo.get_issues(state="open"					)

 for issue in open_issues:
  __a       =  sorted([comment for comment in issue.get_comments()]							,				key=lambda _A					: i.created_at							,				reverse=_A					)
  __a       =  comments[0] if len(_A					) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()					)
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="closed"					)
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()					)
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       "This issue has been automatically marked as stale because it has not had "
       "recent activity. If you think this still needs to be addressed "
       "please comment on this thread.\n\nPlease note that issues that do not follow the "
       "[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
       "are likely to be ignored."					)


if __name__ == "__main__":
  main()


