






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


UpperCAmelCase__				   =		get_logger(__name__)


UpperCAmelCase__				   =		Path(__file__).parent / "model_card_template.md"
UpperCAmelCase__				   =		uuida().hex
UpperCAmelCase__				   =		os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__				   =		os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
UpperCAmelCase__				   =		HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"
def 						_a    (      a :Union[Dict, str, None] = None )							->			str:
 a		  =      F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
 if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
  return ua + "; telemetry/off"
 if is_torch_available():
  ua += F"""; torch/{_torch_version}"""
 if is_flax_available():
  ua += F"""; jax/{_jax_version}"""
  ua += F"""; flax/{_flax_version}"""
 if is_onnx_available():
  ua += F"""; onnxruntime/{_onnxruntime_version}"""
 # CI will set this value to True
 if os.environ.get('''DIFFUSERS_IS_CI'''	,							'''''' ).upper() in ENV_VARS_TRUE_VALUES:
  ua += "; is_ci/true"
 if isinstance(a	,							a ):
  ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items() )
 elif isinstance(a	,							a ):
  ua += "; " + user_agent
 return ua
def 						_a    (      a :str	,							a :Optional[str] = None	,							a :Optional[str] = None )							->			Union[str, Any]:
 if token is None:
  a		  =      HfFolder.get_token()
 if organization is None:
  a		  =      whoami(a )['''name''']
  return F"""{username}/{model_id}"""
 else:
  return F"""{organization}/{model_id}"""
def 						_a    (      a :List[Any]	,							a :Tuple )							->			List[str]:
 if not is_jinja_available():
  raise ValueError(
      '''Modelcard rendering is based on Jinja templates.'''
      ''' Please make sure to have `jinja` installed before using `create_model_card`.'''
      ''' To install it, please run `pip install Jinja2`.''' )

 if hasattr(a	,							'''local_rank''' ) and args.local_rank not in [-1, 0]:
  return

 a		  =      args.hub_token if hasattr(a	,							'''hub_token''' ) else None
 a		  =      get_full_repo_name(a	,							token=a )

 a		  =      ModelCard.from_template(
     card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
         language='''en'''	,							license='''apache-2.0'''	,							library_name='''diffusers'''	,							tags=[]	,							datasets=args.dataset_name	,							metrics=[]	,							)	,							template_path=a	,							model_name=a	,							repo_name=a	,							dataset_name=args.dataset_name if hasattr(a	,							'''dataset_name''' ) else None	,							learning_rate=args.learning_rate	,							train_batch_size=args.train_batch_size	,							eval_batch_size=args.eval_batch_size	,							gradient_accumulation_steps=(
         args.gradient_accumulation_steps if hasattr(a	,							'''gradient_accumulation_steps''' ) else None
     )	,							adam_betaa=args.adam_betaa if hasattr(a	,							'''adam_beta1''' ) else None	,							adam_betaa=args.adam_betaa if hasattr(a	,							'''adam_beta2''' ) else None	,							adam_weight_decay=args.adam_weight_decay if hasattr(a	,							'''adam_weight_decay''' ) else None	,							adam_epsilon=args.adam_epsilon if hasattr(a	,							'''adam_epsilon''' ) else None	,							lr_scheduler=args.lr_scheduler if hasattr(a	,							'''lr_scheduler''' ) else None	,							lr_warmup_steps=args.lr_warmup_steps if hasattr(a	,							'''lr_warmup_steps''' ) else None	,							ema_inv_gamma=args.ema_inv_gamma if hasattr(a	,							'''ema_inv_gamma''' ) else None	,							ema_power=args.ema_power if hasattr(a	,							'''ema_power''' ) else None	,							ema_max_decay=args.ema_max_decay if hasattr(a	,							'''ema_max_decay''' ) else None	,							mixed_precision=args.mixed_precision	,							)

 a		  =      os.path.join(args.output_dir	,							'''README.md''' )
 model_card.save(a )
def 						_a    (      a :Optional[str]	,							a :Optional[str] = None )							->			Optional[int]:
 if resolved_file is None or commit_hash is not None:
  return commit_hash
 a		  =      str(Path(a ).as_posix() )
 a		  =      re.search(r'''snapshots/([^/]+)/'''	,							a )
 if search is None:
  return None
 a		  =      search.groups()[0]
 return commit_hash if REGEX_COMMIT_HASH.match(a ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
UpperCAmelCase__				   =		os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCAmelCase__				   =		os.path.join(hf_cache_home, "diffusers")
def 						_a    (      a :Optional[str] = None	,							a :Optional[str] = None )							->			None:
 if new_cache_dir is None:
  a		  =      DIFFUSERS_CACHE
 if old_cache_dir is None:
  a		  =      old_diffusers_cache

 a		  =      Path(a ).expanduser()
 a		  =      Path(a ).expanduser()
 for old_blob_path in old_cache_dir.glob('''**/blobs/*''' ):
  if old_blob_path.is_file() and not old_blob_path.is_symlink():
   a		  =      new_cache_dir / old_blob_path.relative_to(a )
   new_blob_path.parent.mkdir(parents=a	,							exist_ok=a )
   os.replace(a	,							a )
   try:
    os.symlink(a	,							a )
   except OSError:
    logger.warning(
        '''Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.''' )
 # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


UpperCAmelCase__				   =		os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
     UpperCAmelCase__				   =		0
else:
     with open(cache_version_file) as f:
          try:
               UpperCAmelCase__				   =		int(f.read())
          except ValueError:
               UpperCAmelCase__				   =		0

if cache_version < 1:
     UpperCAmelCase__				   =		os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
     if old_cache_is_not_empty:
          logger.warning(
              "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
              "existing cached models. This is a one-time operation, you can interrupt it or run it "
              "later by calling `diffusers.utils.hub_utils.move_cache()`."
          )
          try:
               move_cache()
          except Exception as e:
               UpperCAmelCase__				   =		"\n".join(traceback.format_tb(e.__traceback__))
               logger.error(
                   f"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
                   "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
                   "message and we will do our best to help."
               )

if cache_version < 1:
     try:
          os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
          with open(cache_version_file, "w") as f:
               f.write("1")
     except Exception:
          logger.warning(
              f"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
              "the directory exists and can be written to."
          )
def 						_a    (      a :str	,							a :Optional[str] = None )							->			str:
 if variant is not None:
  a		  =      weights_name.split('''.''' )
  a		  =      splits[:-1] + [variant] + splits[-1:]
  a		  =      '''.'''.join(a )

 return weights_name

def 						_a    (      a :int	,							*,
    a :Dict	,							a :Any	,							a :Tuple	,							a :List[Any]	,							a :Union[str, Any]	,							a :Any	,							a :str	,							a :Dict	,							a :str	,							a :str	,							a :List[str]=None	,							)							->			Any:
 a		  =      str(a )
 if os.path.isfile(a ):
  return pretrained_model_name_or_path
 elif os.path.isdir(a ):
  if os.path.isfile(os.path.join(a	,							a ) ):
   # Load from a PyTorch checkpoint
   a		  =      os.path.join(a	,							a )
   return model_file
  elif subfolder is not None and os.path.isfile(
      os.path.join(a	,							a	,							a ) ):
   a		  =      os.path.join(a	,							a	,							a )
   return model_file
  else:
   raise EnvironmentError(
       F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.""" )
 else:
  # 1. First check if deprecated way of loading from branches is used
  if (
      revision in DEPRECATED_REVISION_ARGS
      and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
      and version.parse(version.parse(a ).base_version ) >= version.parse('''0.20.0''' )
  ):
   try:
    a		  =      hf_hub_download(
        a	,							filename=_add_variant(a	,							a )	,							cache_dir=a	,							force_download=a	,							proxies=a	,							resume_download=a	,							local_files_only=a	,							use_auth_token=a	,							user_agent=a	,							subfolder=a	,							revision=revision or commit_hash	,							)
    warnings.warn(
        F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""	,							a	,							)
    return model_file
   except:  # noqa: E722
    warnings.warn(
        F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a	,							a )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a	,							a )}' so that the correct variant file can be added."""	,							a	,							)
  try:
   # 2. Load model file as usual
   a		  =      hf_hub_download(
       a	,							filename=a	,							cache_dir=a	,							force_download=a	,							proxies=a	,							resume_download=a	,							local_files_only=a	,							use_auth_token=a	,							user_agent=a	,							subfolder=a	,							revision=revision or commit_hash	,							)
   return model_file

  except RepositoryNotFoundError:
   raise EnvironmentError(
       F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
       '''listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '''
       '''token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '''
       '''login`.''' )
  except RevisionNotFoundError:
   raise EnvironmentError(
       F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
       '''this model name. Check the model page at '''
       F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions.""" )
  except EntryNotFoundError:
   raise EnvironmentError(
       F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.""" )
  except HTTPError as err:
   raise EnvironmentError(
       F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}""" )
  except ValueError:
   raise EnvironmentError(
       F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
       F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
       F""" directory containing a file named {weights_name} or"""
       ''' \nCheckout your internet connection or see how to run the library in'''
       ''' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.''' )
  except EnvironmentError:
   raise EnvironmentError(
       F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
       '''\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '''
       F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
       F"""containing a file named {weights_name}""" )








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     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=10			,						lowerCamelCase__=3			,						lowerCamelCase__=2			,						lowerCamelCase__=2			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=32			,						lowerCamelCase__=5			,						lowerCamelCase__=4			,						lowerCamelCase__=37			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=10			,						lowerCamelCase__=0.02			,						lowerCamelCase__="divided_space_time"			,						lowerCamelCase__=None			,						)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_frames
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    hidden_size
							__lowerCamelCase		       =    num_hidden_layers
							__lowerCamelCase		       =    num_attention_heads
							__lowerCamelCase		       =    intermediate_size
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    attention_type
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    scope
							__lowerCamelCase		       =    num_labels

							# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
							__lowerCamelCase		       =    (image_size // patch_size) ** 2
							__lowerCamelCase		       =    (num_frames) * self.num_patches_per_frame + 1


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    floats_tensor(
							    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

							__lowerCamelCase		       =    None
							if self.use_labels:
											__lowerCamelCase		       =    ids_tensor([self.batch_size]			,						self.num_labels							)

							__lowerCamelCase		       =    self.get_config()

							return config, pixel_values, labels


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    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			,						)
							__lowerCamelCase		       =    self.num_labels
							return config


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__							)
							self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()

							__lowerCamelCase		       =    model(lowerCamelCase__							)

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






			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    self.prepare_config_and_inputs()
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    config_and_inputs
							__lowerCamelCase		       =    {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			snake_case_   		=       (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
			snake_case_   		=       (
			    {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False
			snake_case_   		=       False


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(
							    self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__			,						hidden_size=37							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    copy.deepcopy(lowerCamelCase__							)

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

							return inputs_dict


			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			@unittest.skip(reason='TimeSformer does not use inputs_embeds'							)
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

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


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase		       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      str:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__							)


			@slow
			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    TimesformerModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							if not self.has_attentions:
											pass

							else:
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCamelCase		       =    True

											for model_class in self.all_model_classes:
															__lowerCamelCase		       =    self.model_tester.seq_length
															__lowerCamelCase		       =    self.model_tester.num_frames

															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    False
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    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"]
															__lowerCamelCase		       =    True
															__lowerCamelCase		       =    model_class(lowerCamelCase__							)
															model.to(lowerCamelCase__							)
															model.eval()
															with torch.no_grad():
																			__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)
															__lowerCamelCase		       =    outputs.attentions
															self.assertEqual(len(lowerCamelCase__							)			,						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]			,						)
															__lowerCamelCase		       =    len(lowerCamelCase__							)

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

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

															__lowerCamelCase		       =    outputs.attentions

															self.assertEqual(len(lowerCamelCase__							)			,						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							)    ->      Tuple:



							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											model.to(lowerCamelCase__							)
											model.eval()

											with torch.no_grad():
															__lowerCamelCase		       =    model(**self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__							)							)

											__lowerCamelCase		       =    outputs.hidden_states
											__lowerCamelCase		       =    self.model_tester.num_hidden_layers + 1
											self.assertEqual(len(lowerCamelCase__							)			,						lowerCamelCase__							)

											__lowerCamelCase		       =    self.model_tester.seq_length

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

							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    True
											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)

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

											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)







def 			lowerCamelCase_		(       )     ->       Any:





				"""simple docstring"""


				__lowerCamelCase		       =    hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video' ,     filename='eating_spaghetti.npy' ,     repo_type='dataset'      )
				__lowerCamelCase		       =    np.load(UpperCamelCase__      )
				return list(UpperCamelCase__      )







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

			"""simple docstring"""







			@cached_property
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							# 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							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    TimesformerForVideoClassification.from_pretrained('facebook/timesformer-base-finetuned-k400'							).to(
							    lowerCamelCase__							)

							__lowerCamelCase		       =    self.default_image_processor
							__lowerCamelCase		       =    prepare_video()
							__lowerCamelCase		       =    image_processor(video[:8]			,						return_tensors='pt'							).to(lowerCamelCase__							)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase		       =    model(**lowerCamelCase__							)

							# verify the logits
							__lowerCamelCase		       =    torch.Size((1, 400)							)
							self.assertEqual(outputs.logits.shape			,						lowerCamelCase__							)

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

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


