



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       _SCREAMING_SNAKE_CASE	:





     def __init__(       self :							Union[str, Any]     ,  UpperCamelCase :							int     ,  UpperCamelCase :							Any=1_3     ,  UpperCamelCase :							List[Any]=1_0     ,  UpperCamelCase :							List[str]=3     ,  UpperCamelCase :							Any=2     ,  UpperCamelCase :							Tuple=2     ,  UpperCamelCase :							Tuple=True     ,  UpperCamelCase :							List[str]=True     ,  UpperCamelCase :							Any=3_2     ,  UpperCamelCase :							int=5     ,  UpperCamelCase :							Any=4     ,  UpperCamelCase :							int=3_7     ,  UpperCamelCase :							Optional[int]="gelu"     ,  UpperCamelCase :							List[Any]=0.1     ,  UpperCamelCase :							List[str]=0.1     ,  UpperCamelCase :							Optional[Any]=1_0     ,  UpperCamelCase :							List[Any]=0.0_2     ,  UpperCamelCase :							List[Any]="divided_space_time"     ,  UpperCamelCase :							Optional[int]=None     ,  )->List[Any]:
      __SCREAMING_SNAKE_CASE       :    int       =	parent
      __SCREAMING_SNAKE_CASE       :    str       =	batch_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	image_size
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	num_channels
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	patch_size
      __SCREAMING_SNAKE_CASE       :    Dict       =	num_frames
      __SCREAMING_SNAKE_CASE       :    Any       =	is_training
      __SCREAMING_SNAKE_CASE       :    Any       =	use_labels
      __SCREAMING_SNAKE_CASE       :    List[str]       =	hidden_size
      __SCREAMING_SNAKE_CASE       :    str       =	num_hidden_layers
      __SCREAMING_SNAKE_CASE       :    str       =	num_attention_heads
      __SCREAMING_SNAKE_CASE       :    Tuple       =	intermediate_size
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	hidden_act
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hidden_dropout_prob
      __SCREAMING_SNAKE_CASE       :    int       =	attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	attention_type
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	initializer_range
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	scope
      __SCREAMING_SNAKE_CASE       :    Tuple       =	num_labels

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





     def 	__snake_case    (       self :							str				)->str:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]				)

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

      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self.get_config()

      return config, pixel_values, labels





     def 	__snake_case    (       self :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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     ,  )
      __SCREAMING_SNAKE_CASE       :    str       =	self.num_labels
      return config





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Dict     ,  UpperCamelCase :							Optional[int]				)->str:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	TimesformerModel(config=UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	model(UpperCamelCase				)
      self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[int]     ,  UpperCamelCase :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    List[str]       =	TimesformerForVideoClassification(UpperCamelCase				)
      model.to(UpperCamelCase				)
      model.eval()

      __SCREAMING_SNAKE_CASE       :    Tuple       =	model(UpperCamelCase				)

      # verify the logits shape
      __SCREAMING_SNAKE_CASE       :    Tuple       =	torch.Size((self.batch_size, self.num_labels)				)
      self.parent.assertEqual(result.logits.shape     ,  UpperCamelCase				)






     def 	__snake_case    (       self :							Optional[int]				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	config_and_inputs
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	{"pixel_values": pixel_values}
      return config, inputs_dict






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

     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False
     lowerCAmelCase					=			False





     def 	__snake_case    (       self :							Dict				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	TimesformerModelTester(self				)
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	ConfigTester(
          self     ,  config_class=UpperCamelCase     ,  has_text_modality=UpperCamelCase     ,  hidden_size=3_7				)





     def 	__snake_case    (       self :							List[Any]     ,  UpperCamelCase :							List[Any]     ,  UpperCamelCase :							Tuple     ,  UpperCamelCase :							int=False				)->Optional[Any]:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	copy.deepcopy(UpperCamelCase				)

      if return_labels:
       if model_class in get_values(UpperCamelCase				):
        __SCREAMING_SNAKE_CASE       :    List[str]       =	torch.zeros(
            self.model_tester.batch_size     ,  dtype=torch.long     ,  device=UpperCamelCase				)

      return inputs_dict





     def 	__snake_case    (       self :							Optional[Any]				)->Tuple:
      self.config_tester.run_common_tests()





     @unittest.skip(reason="TimeSformer does not use inputs_embeds"				)
     def 	__snake_case    (       self :							Any				)->Optional[Any]:
      pass





     def 	__snake_case    (       self :							Any				)->Any:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Dict       =	model_class(UpperCamelCase				)
       self.assertIsInstance(model.get_input_embeddings()     ,  (nn.Module)				)
       __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(UpperCamelCase     ,  nn.Linear				)				)





     def 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

       __SCREAMING_SNAKE_CASE       :    Tuple       =	["pixel_values"]
       self.assertListEqual(arg_names[:1]     ,  UpperCamelCase				)





     def 	__snake_case    (       self :							Any				)->int:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*UpperCamelCase				)





     @slow
     def 	__snake_case    (       self :							Any				)->Union[str, Any]:
      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __SCREAMING_SNAKE_CASE       :    str       =	TimesformerModel.from_pretrained(UpperCamelCase				)
       self.assertIsNotNone(UpperCamelCase				)





     def 	__snake_case    (       self :							int				)->Tuple:
      if not self.has_attentions:
       pass

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

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	self.model_tester.seq_length
        __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.num_frames

        __SCREAMING_SNAKE_CASE       :    List[Any]       =	True
        __SCREAMING_SNAKE_CASE       :    Tuple       =	False
        __SCREAMING_SNAKE_CASE       :    int       =	True
        __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	model_class(UpperCamelCase				)
        model.to(UpperCamelCase				)
        model.eval()
        with torch.no_grad():
         __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
        __SCREAMING_SNAKE_CASE       :    Tuple       =	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"]
        __SCREAMING_SNAKE_CASE       :    Dict       =	True
        __SCREAMING_SNAKE_CASE       :    Any       =	model_class(UpperCamelCase				)
        model.to(UpperCamelCase				)
        model.eval()
        with torch.no_grad():
         __SCREAMING_SNAKE_CASE       :    Dict       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)
        __SCREAMING_SNAKE_CASE       :    Tuple       =	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]     ,  )
        __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	len(UpperCamelCase				)

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

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

        __SCREAMING_SNAKE_CASE       :    str       =	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 	__snake_case    (       self :							Tuple				)->Union[str, Any]:
      def check_hidden_states_output(UpperCamelCase :							int     ,  UpperCamelCase :							List[str]     ,  UpperCamelCase :							Tuple				):
       __SCREAMING_SNAKE_CASE       :    Dict       =	model_class(UpperCamelCase				)
       model.to(UpperCamelCase				)
       model.eval()

       with torch.no_grad():
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	model(**self._prepare_for_class(UpperCamelCase     ,  UpperCamelCase				)				)

       __SCREAMING_SNAKE_CASE       :    int       =	outputs.hidden_states
       __SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.num_hidden_layers + 1
       self.assertEqual(len(UpperCamelCase				)     ,  UpperCamelCase				)

       __SCREAMING_SNAKE_CASE       :    List[str]       =	self.model_tester.seq_length

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

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __SCREAMING_SNAKE_CASE       :    Dict       =	True
       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       __SCREAMING_SNAKE_CASE       :    List[str]       =	True

       check_hidden_states_output(UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase				)






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video"		,	filename="eating_spaghetti.npy"		,	repo_type="dataset"     )
 __SCREAMING_SNAKE_CASE       :    int       =	np.load(__lowerCamelCase     )
 return list(__lowerCamelCase     )






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





     @cached_property
     def 	__snake_case    (       self :							Optional[int]				)->Optional[Any]:
      # 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 	__snake_case    (       self :							int				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"				).to(
          UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	self.default_image_processor
      __SCREAMING_SNAKE_CASE       :    Dict       =	prepare_video()
      __SCREAMING_SNAKE_CASE       :    str       =	image_processor(video[:8]     ,  return_tensors="pt"				).to(UpperCamelCase				)

      # forward pass
      with torch.no_grad():
       __SCREAMING_SNAKE_CASE       :    Any       =	model(**UpperCamelCase				)

      # verify the logits
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.Size((1, 4_0_0)				)
      self.assertEqual(outputs.logits.shape     ,  UpperCamelCase				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]				).to(UpperCamelCase				)

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











from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers






