




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:






    """simple docstring"""





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

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





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Any  =     floats_tensor(
             [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

         UpperCAmelCase_     :				Any  =     self.get_config()

         return config, pixel_values, labels





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				List[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  ,)
         UpperCAmelCase_     :				Any  =     self.num_labels
         return config





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[Any]:
         UpperCAmelCase_     :				List[str]  =     TimesformerModel(config=_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()
         UpperCAmelCase_     :				Optional[Any]  =     model(_SCREAMING_SNAKE_CASE    )
         self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )					->						Optional[int]:
         UpperCAmelCase_     :				int  =     TimesformerForVideoClassification(_SCREAMING_SNAKE_CASE    )
         model.to(_SCREAMING_SNAKE_CASE    )
         model.eval()

         UpperCAmelCase_     :				List[Any]  =     model(_SCREAMING_SNAKE_CASE    )

         # verify the logits shape
         UpperCAmelCase_     :				Optional[Any]  =     torch.Size((self.batch_size, self.num_labels)    )
         self.parent.assertEqual(result.logits.shape  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				int  =     self.prepare_config_and_inputs()
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Dict  =     config_and_inputs
         UpperCAmelCase_     :				Tuple  =     {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class     __a(	_a							,		_a							,		unittest.TestCase  ):






    """simple docstring"""





    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 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Dict  =     TimesformerModelTester(self    )
         UpperCAmelCase_     :				str  =     ConfigTester(
             self  ,config_class=_SCREAMING_SNAKE_CASE  ,has_text_modality=_SCREAMING_SNAKE_CASE  ,hidden_size=37    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=False    )					->						List[Any]:
         UpperCAmelCase_     :				List[str]  =     copy.deepcopy(_SCREAMING_SNAKE_CASE    )

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

         return inputs_dict





    def 				a__ ( self    )					->						Union[str, Any]:
         self.config_tester.run_common_tests()





    @unittest.skip(reason='''TimeSformer does not use inputs_embeds'''    )
    def 				a__ ( self    )					->						Union[str, Any]:
         pass





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_,       UpperCAmelCase_     :				int  =     self.model_tester.prepare_config_and_inputs_for_common()

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





    def 				a__ ( self    )					->						List[Any]:
         UpperCAmelCase_,       UpperCAmelCase_     :				List[str]  =     self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase_     :				Optional[int]  =     ['''pixel_values''']
              self.assertListEqual(arg_names[:1]  ,_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				str  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Dict  =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_video_classification(*_SCREAMING_SNAKE_CASE    )





    @slow
    def 				a__ ( self    )					->						List[Any]:
         for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_     :				Optional[Any]  =     TimesformerModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self    )					->						List[str]:
         if not self.has_attentions:
              pass

         else:
              UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     self.model_tester.prepare_config_and_inputs_for_common()
              UpperCAmelCase_     :				Optional[int]  =     True

              for model_class in self.all_model_classes:
                   UpperCAmelCase_     :				List[Any]  =     self.model_tester.seq_length
                   UpperCAmelCase_     :				Dict  =     self.model_tester.num_frames

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

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

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

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

                   UpperCAmelCase_     :				str  =     outputs.attentions

                   self.assertEqual(len(_SCREAMING_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 				a__ ( self    )					->						Optional[int]:
         def check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				List[Any]  =     model_class(_SCREAMING_SNAKE_CASE    )
              model.to(_SCREAMING_SNAKE_CASE    )
              model.eval()

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

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

              UpperCAmelCase_     :				List[Any]  =     self.model_tester.seq_length

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

         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[Any]  =     self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_     :				Any  =     True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_     :				Optional[int]  =     True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''					,		filename='''eating_spaghetti.npy'''					,		repo_type='''dataset''' )
     UpperCAmelCase_     :				List[Any]  =     np.load(_lowercase )
     return list(_lowercase )



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






    """simple docstring"""





    @cached_property
    def 				a__ ( self    )					->						List[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 				a__ ( self    )					->						int:
         UpperCAmelCase_     :				Any  =     TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''    ).to(
             _SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Union[str, Any]  =     self.default_image_processor
         UpperCAmelCase_     :				Optional[Any]  =     prepare_video()
         UpperCAmelCase_     :				List[str]  =     image_processor(video[:8]  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Dict  =     model(**_SCREAMING_SNAKE_CASE    )

         # verify the logits
         UpperCAmelCase_     :				List[str]  =     torch.Size((1, 400)    )
         self.assertEqual(outputs.logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[Any]  =     torch.tensor([-0.30_16, -0.77_13, -0.42_05]    ).to(_SCREAMING_SNAKE_CASE    )

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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
							from ..ta.tokenization_ta import TaTokenizer
else:
							from ...utils.dummy_sentencepiece_objects import TaTokenizer

__SCREAMING_SNAKE_CASE					:				str									=		TaTokenizer

if is_tokenizers_available():
							from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
							from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Any									=		TaTokenizerFast

__SCREAMING_SNAKE_CASE					:				Tuple									=		{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[Any]									=		[
							    '''MT5EncoderModel''',
							    '''MT5ForConditionalGeneration''',
							    '''MT5ForQuestionAnswering''',
							    '''MT5Model''',
							    '''MT5PreTrainedModel''',
							    '''MT5Stack''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				str									=		['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE					:				List[str]									=		['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
							from .configuration_mta import MTaConfig, MTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mta import (
														    MTaEncoderModel,
														    MTaForConditionalGeneration,
														    MTaForQuestionAnswering,
														    MTaModel,
														    MTaPreTrainedModel,
														    MTaStack,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
							import sys

							__SCREAMING_SNAKE_CASE					:				Union[str, Any]									=		_LazyModule(
							    __name__,
							    globals()['''__file__'''],
							    _import_structure,
							    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
							    module_spec=__spec__,
							)





