

"""simple docstring"""





from typing import Mapping

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


__UpperCamelCase  :      List[Any]							      =		logging.get_logger(__name__)

__UpperCamelCase  :      List[Any]							      =		{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       "t5"
					lowercase__				 =       ["past_key_values"]
					lowercase__				 =       {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(       self			:				Dict   ,lowercase_			:				List[Any]=3_2_1_2_8   ,lowercase_			:				List[str]=5_1_2   ,lowercase_			:				List[str]=6_4   ,lowercase_			:				str=2_0_4_8   ,lowercase_			:				Any=6   ,lowercase_			:				Any=None   ,lowercase_			:				Any=8   ,lowercase_			:				List[Any]=3_2   ,lowercase_			:				Dict=1_2_8   ,lowercase_			:				List[Any]=0.1   ,lowercase_			:				Any=1E-6   ,lowercase_			:				Any=1.0   ,lowercase_			:				List[Any]="relu"   ,lowercase_			:				List[str]=True   ,lowercase_			:				List[Any]=True   ,lowercase_			:				Union[str, Any]=0   ,lowercase_			:				Tuple=1   ,**lowercase_			:				Optional[int]   ,):
						lowerCAmelCase__	:						List[str]  				=	vocab_size
						lowerCAmelCase__	:						Union[str, Any]  				=	d_model
						lowerCAmelCase__	:						int  				=	d_kv
						lowerCAmelCase__	:						str  				=	d_ff
						lowerCAmelCase__	:						Optional[Any]  				=	num_layers
						lowerCAmelCase__	:						Any  				=	(
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__	:						Dict  				=	num_heads
						lowerCAmelCase__	:						Optional[Any]  				=	relative_attention_num_buckets
						lowerCAmelCase__	:						Optional[Any]  				=	relative_attention_max_distance
						lowerCAmelCase__	:						Any  				=	dropout_rate
						lowerCAmelCase__	:						Optional[Any]  				=	layer_norm_epsilon
						lowerCAmelCase__	:						Dict  				=	initializer_factor
						lowerCAmelCase__	:						str  				=	feed_forward_proj
						lowerCAmelCase__	:						List[Any]  				=	use_cache

						lowerCAmelCase__	:						Optional[Any]  				=	self.feed_forward_proj.split('''-'''		)
						lowerCAmelCase__	:						Tuple  				=	act_info[-1]
						lowerCAmelCase__	:						List[Any]  				=	act_info[0] == '''gated'''

						if len(lowercase_		) > 1 and act_info[0] != "gated" or len(lowercase_		) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''		)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__	:						Optional[Any]  				=	'''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_   ,eos_token_id=lowercase_   ,is_encoder_decoder=lowercase_   ,**lowercase_   ,)







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					@property
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						List[Any]  				=	{
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__	:						Tuple  				=	'''past_encoder_sequence + sequence'''
							lowerCAmelCase__	:						List[Any]  				=	{0: '''batch'''}
							lowerCAmelCase__	:						List[str]  				=	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__	:						Optional[Any]  				=	{0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__	:						Dict  				=	{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_   ,direction='''inputs'''		)

						return common_inputs







					@property
					def        __lowerCAmelCase (       self			:				Any		):
						return 1_3







"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				int   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(
						    repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Tuple  				=	VideoClassificationPipeline(model=lowercase_   ,image_processor=lowercase_   ,top_k=2		)
						lowerCAmelCase__	:						Optional[int]  				=	[
						    example_video_filepath,
						    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
						]
						return video_classifier, examples







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Dict		):
						for example in examples:
							lowerCAmelCase__	:						Dict  				=	video_classifier(lowercase_		)

							self.assertEqual(
							    lowercase_   ,[
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							    ]   ,)







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
						lowerCAmelCase__	:						List[str]  				=	VideoMAEFeatureExtractor(
						    size={'''shortest_edge''': 1_0}   ,crop_size={'''height''': 1_0, '''width''': 1_0}		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipeline(
						    '''video-classification'''   ,model=lowercase_   ,feature_extractor=lowercase_   ,frame_sampling_rate=4		)

						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Optional[int]  				=	video_classifier(lowercase_   ,top_k=2		)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   ,)

						lowerCAmelCase__	:						Dict  				=	video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]   ,top_k=2   ,)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						    ]   ,)







					@require_tf
					def        __lowerCAmelCase (       self			:				int		):
						pass




