






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase        =							logging.get_logger(__name__)

__lowerCAmelCase        =							{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class   __SCREAMING_SNAKE_CASE       (UpperCAmelCase__       ):


  """simple docstring"""


  _a  : Optional[int]      			=     '''decision_transformer'''
  _a  : int      			=     ['''past_key_values''']
  _a  : int      			=     {
      '''max_position_embeddings''': '''n_positions''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }







  def __init__(			self   ,	UpperCamelCase__=17   ,	UpperCamelCase__=4   ,	UpperCamelCase__=128   ,	UpperCamelCase__=4_096   ,	UpperCamelCase__=True   ,	UpperCamelCase__=1   ,	UpperCamelCase__=1_024   ,	UpperCamelCase__=3   ,	UpperCamelCase__=1   ,	UpperCamelCase__=None   ,	UpperCamelCase__="relu"   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=0.1   ,	UpperCamelCase__=1e-5   ,	UpperCamelCase__=0.02   ,	UpperCamelCase__=True   ,	UpperCamelCase__=True   ,	UpperCamelCase__=50_256   ,	UpperCamelCase__=50_256   ,	UpperCamelCase__=False   ,	UpperCamelCase__=False   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			state_dim
         a_						     =			act_dim
         a_						     =			hidden_size
         a_						     =			max_ep_len
         a_						     =			action_tanh
         a_						     =			vocab_size
         a_						     =			n_positions
         a_						     =			n_layer
         a_						     =			n_head
         a_						     =			n_inner
         a_						     =			activation_function
         a_						     =			resid_pdrop
         a_						     =			embd_pdrop
         a_						     =			attn_pdrop
         a_						     =			layer_norm_epsilon
         a_						     =			initializer_range
         a_						     =			scale_attn_weights
         a_						     =			use_cache
         a_						     =			scale_attn_by_inverse_layer_idx
         a_						     =			reorder_and_upcast_attn

         a_						     =			bos_token_id
         a_						     =			eos_token_id

         super().__init__(bos_token_id=_UpperCAmelCase   ,	eos_token_id=_UpperCAmelCase   ,	**_UpperCAmelCase	)








import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY



@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					A__     =					MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Tuple						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						hf_hub_download(
							    repo_id="""nateraw/video-demo""" ,   filename="""archery.mp4""" ,   repo_type="""dataset"""						)
							lowercase__	     =						VideoClassificationPipeline(model=_UpperCAmelCase ,   image_processor=_UpperCAmelCase ,   top_k=2						)
							lowercase__	     =						[
							    example_video_filepath,
							    """https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4""",
							]
							return video_classifier, examples

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							for example in examples:
									lowercase__	     =						video_classifier(_UpperCAmelCase						)

									self.assertEqual(
									    _UpperCAmelCase ,   [
									        {"""score""": ANY(_UpperCAmelCase						), """label""": ANY(_UpperCAmelCase						)},
									        {"""score""": ANY(_UpperCAmelCase						), """label""": ANY(_UpperCAmelCase						)},
									    ] ,   )

					@require_torch
					def     lowerCamelCase__	(self       :		str						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						"""hf-internal-testing/tiny-random-VideoMAEForVideoClassification"""
							lowercase__	     =						VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10} ,   crop_size={"""height""": 10, """width""": 10}						)
							lowercase__	     =						pipeline(
							    """video-classification""" ,   model=_UpperCAmelCase ,   feature_extractor=_UpperCAmelCase ,   frame_sampling_rate=4						)

							lowercase__	     =						hf_hub_download(repo_id="""nateraw/video-demo""" ,   filename="""archery.mp4""" ,   repo_type="""dataset"""						)
							lowercase__	     =						video_classifier(_UpperCAmelCase ,   top_k=2						)
							self.assertEqual(
							    nested_simplify(_UpperCAmelCase ,   decimals=4						) ,   [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}] ,   )

							lowercase__	     =						video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ] ,   top_k=2 ,   )
							self.assertEqual(
							    nested_simplify(_UpperCAmelCase ,   decimals=4						) ,   [
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							        [{"""score""": 0.5_199, """label""": """LABEL_0"""}, {"""score""": 0.4_801, """label""": """LABEL_1"""}],
							    ] ,   )


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




							"""simple docstring"""


							pass

