



"""simple docstring"""

import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def __lowercase			(							_a    ):
    snake_case_ :     List[str]				=	[
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a    ):
    snake_case_ :     Any				=	list(s_dict.keys()    )
    for key in keys:
        if "transformer_layers" in key:
            snake_case_ :     str				=	s_dict.pop(_a    )
        elif "subsample" in key:
            snake_case_ :     Optional[Any]				=	s_dict.pop(_a    )




def __lowercase			(							_a    ):
    snake_case_,	snake_case_ :     List[str]				=	emb.weight.shape
    snake_case_ :     List[Any]				=	nn.Linear(_a		,			_a		,			bias=_a    )
    snake_case_ :     List[Any]				=	emb.weight.data
    return lin_layer




def __lowercase			(							_a		,			_a    ):
    snake_case_ :     List[Any]				=	torch.load(_a		,			map_location='''cpu'''    )
    snake_case_ :     Dict				=	mam_aaa['''args''']
    snake_case_ :     str				=	mam_aaa['''model''']
    snake_case_ :     Union[str, Any]				=	state_dict['''decoder.output_projection.weight''']

    remove_ignore_keys_(_a    )
    rename_keys(_a    )

    snake_case_ :     Dict				=	state_dict['''decoder.embed_tokens.weight'''].shape[0]

    snake_case_ :     List[Any]				=	args.share_decoder_input_output_embed

    snake_case_ :     List[Any]				=	[int(_a    ) for i in args.conv_kernel_sizes.split(''','''    )]
    snake_case_ :     List[str]				=	SpeechaTextConfig(
        vocab_size=_a		,			max_source_positions=args.max_source_positions		,			max_target_positions=args.max_target_positions		,			encoder_layers=args.encoder_layers		,			decoder_layers=args.decoder_layers		,			encoder_attention_heads=args.encoder_attention_heads		,			decoder_attention_heads=args.decoder_attention_heads		,			encoder_ffn_dim=args.encoder_ffn_embed_dim		,			decoder_ffn_dim=args.decoder_ffn_embed_dim		,			d_model=args.encoder_embed_dim		,			dropout=args.dropout		,			attention_dropout=args.attention_dropout		,			activation_dropout=args.activation_dropout		,			activation_function='''relu'''		,			num_conv_layers=len(_a    )		,			conv_channels=args.conv_channels		,			conv_kernel_sizes=_a		,			input_feat_per_channel=args.input_feat_per_channel		,			input_channels=args.input_channels		,			tie_word_embeddings=_a		,			num_beams=5		,			max_length=200		,			use_cache=_a		,			decoder_start_token_id=2		,			early_stopping=_a		,			)

    snake_case_ :     Any				=	SpeechaTextForConditionalGeneration(_a    )
    snake_case_,	snake_case_ :     List[str]				=	model.model.load_state_dict(_a		,			strict=_a    )
    if len(_a    ) > 0 and not set(_a    ) <= {
        "encoder.embed_positions.weights",
        "decoder.embed_positions.weights",
    }:
        raise ValueError(
            '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
            f" but all the following weights are missing {missing}"    )

    if tie_embeds:
        snake_case_ :     Any				=	make_linear_from_emb(model.model.decoder.embed_tokens    )
    else:
        snake_case_ :     Optional[Any]				=	lm_head_weights

    model.save_pretrained(_a    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     lowercase__    :  Dict							    =	parser.parse_args()
     convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







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 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

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



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

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

				UpperCamelCase		=     video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]				,      top_k=2				,      )
				self.assertEqual(
				    nested_simplify(A_				,      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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
