"""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			):
					UpperCamelCase_ :	Optional[Any]			=    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Optional[Any]     ):



							for example in examples:
									lowerCAmelCase__        =   video_classifier(__lowerCamelCase     )

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




					@require_torch
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
							lowerCAmelCase__        =   VideoMAEFeatureExtractor(
							    size={"""shortest_edge""": 10}  ,						crop_size={"""height""": 10, """width""": 10}     )
							lowerCAmelCase__        =   pipeline(
							    """video-classification"""  ,						model=__lowerCamelCase  ,						feature_extractor=__lowerCamelCase  ,						frame_sampling_rate=4     )

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

							lowerCAmelCase__        =   video_classifier(
							    [
							        video_file_path,
							        video_file_path,
							    ]  ,						top_k=2  ,						)
							self.assertEqual(
							    nested_simplify(__lowerCamelCase  ,						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 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							pass





"""simple docstring"""




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   TapasModel(config=lowerCamelCase__     )
		else:
				raise ValueError(f"""Task {task} not supported."""     )

		print(f"""Building PyTorch model from configuration: {config}"""     )
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )

		# Save pytorch-model (weights and configuration)
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		model.save_pretrained(lowerCamelCase__     )

		# Save tokenizer files
		print(f"""Save tokenizer files to {pytorch_dump_path}"""     )
		lowerCAmelCase__        =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,  model_max_length=512     )
		tokenizer.save_pretrained(lowerCamelCase__     )

		print("""Used relative position embeddings:"""			,  model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
			)
			parser.add_argument(
			    "--reset_position_index_per_cell",
			    default=False,
			    action="store_true",
			    help="Whether to use relative position embeddings or not. Defaults to True.",
			)
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--tapas_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained TAPAS model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(
			    args.task,
			    args.reset_position_index_per_cell,
			    args.tf_checkpoint_path,
			    args.tapas_config_file,
			    args.pytorch_dump_path,
			)




