from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase_:					List[str]     =					logging.get_logger(__name__)

lowerCAmelCase_:					Union[str, Any]     =					{
    "microsoft/deberta-v2-xlarge": "https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xxlarge": "https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json",
    "microsoft/deberta-v2-xlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"
    ),
    "microsoft/deberta-v2-xxlarge-mnli": (
        "https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"
    ),
}






class    a__    (   lowercase__	):
   snake_case_							=      "deberta-v2"


   def __init__(     self,				_UpperCAmelCase=12_8100,				_UpperCAmelCase=1536,				_UpperCAmelCase=24,				_UpperCAmelCase=24,				_UpperCAmelCase=6144,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-7,				_UpperCAmelCase=False,				_UpperCAmelCase=-1,				_UpperCAmelCase=0,				_UpperCAmelCase=True,				_UpperCAmelCase=None,				_UpperCAmelCase=0,				_UpperCAmelCase="gelu",				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**UpperCAmelCase__  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							relative_attention
        lowercase__				 =							max_relative_positions
        lowercase__				 =							pad_token_id
        lowercase__				 =							position_biased_input

        # Backwards compatibility
        if type(UpperCAmelCase__  ) == str:
             lowercase__				 =							[x.strip() for x in pos_att_type.lower().split("|"  )]

        lowercase__				 =							pos_att_type
        lowercase__				 =							vocab_size
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							kwargs.get("pooler_hidden_size",				UpperCAmelCase__  )
        lowercase__				 =							pooler_dropout
        lowercase__				 =							pooler_hidden_act






class    a__    (   lowercase__	):


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        if self.task == "multiple-choice":
             lowercase__				 =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
             lowercase__				 =							{0: '''batch''', 1: '''sequence'''}
        if self._config.type_vocab_size > 0:
             return OrderedDict(
                 [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]  )
        else:
             return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 12


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase = -1,				_UpperCAmelCase = -1,				_UpperCAmelCase = -1,				_UpperCAmelCase = False,				_UpperCAmelCase = None,				_UpperCAmelCase = 3,				_UpperCAmelCase = 40,				_UpperCAmelCase = 40,				_UpperCAmelCase = None,				):
        '''simple docstring'''



        lowercase__				 =							super().generate_dummy_inputs(preprocessor=UpperCAmelCase__,				framework=UpperCAmelCase__  )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
        return dummy_inputs





"""simple docstring"""


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	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''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 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




