

'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor





def 				A_					(	snake_case ,    snake_case=10 ,    snake_case=10 ,    snake_case=False ,    snake_case=False	):
		SCREAMING_SNAKE_CASE:Optional[Any]   =   []
		for i in range(image_processor_tester.num_frames	):
				video.append(np.random.randint(255 ,    size=(image_processor_tester.num_channels, width, height) ,    dtype=np.uinta	)	)

		if not numpify and not torchify:
				# PIL expects the channel dimension as last dimension
				SCREAMING_SNAKE_CASE:Optional[int]   =   [Image.fromarray(np.moveaxis(_A ,    0 ,    -1	)	) for frame in video]

		if torchify:
				SCREAMING_SNAKE_CASE:int   =   [torch.from_numpy(_A	) for frame in video]

		return video





def 				A_					(	snake_case ,    snake_case=False ,    snake_case=False ,    snake_case=False	):
		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		SCREAMING_SNAKE_CASE:Any   =   []
		for i in range(image_processor_tester.batch_size	):
				if equal_resolution:
						SCREAMING_SNAKE_CASE:Tuple   =   image_processor_tester.max_resolution
				else:
						SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE:List[Any]   =   np.random.choice(
						    np.arange(image_processor_tester.min_resolution ,    image_processor_tester.max_resolution	) ,    2	)
						SCREAMING_SNAKE_CASE:List[Any]   =   prepare_video(
						    image_processor_tester=_A ,    width=_A ,    height=_A ,    numpify=_A ,    torchify=_A ,    )
				video_inputs.append(_A	)

		return video_inputs





class 							_snake_case	(    unittest.TestCase  ):
			def __init__(	self     : Any       ,SCREAMING_SNAKE_CASE__     : Dict       ,SCREAMING_SNAKE_CASE__     : Optional[Any]=7       ,SCREAMING_SNAKE_CASE__     : Tuple=3       ,SCREAMING_SNAKE_CASE__     : Any=4       ,SCREAMING_SNAKE_CASE__     : str=18       ,SCREAMING_SNAKE_CASE__     : int=30       ,SCREAMING_SNAKE_CASE__     : Dict=400       ,SCREAMING_SNAKE_CASE__     : int=True       ,SCREAMING_SNAKE_CASE__     : Dict=None       ,SCREAMING_SNAKE_CASE__     : Optional[int]=True       ,SCREAMING_SNAKE_CASE__     : List[str]=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__     : Optional[int]=[0.5, 0.5, 0.5]       ,SCREAMING_SNAKE_CASE__     : Union[str, Any]=True       ,SCREAMING_SNAKE_CASE__     : Tuple=None       ,):
					SCREAMING_SNAKE_CASE:Optional[int]   =   size if size is not None else {"shortest_edge": 18}
					SCREAMING_SNAKE_CASE:Optional[Any]   =   crop_size if crop_size is not None else {"height": 18, "width": 18}

					SCREAMING_SNAKE_CASE:Optional[int]   =   parent
					SCREAMING_SNAKE_CASE:str   =   batch_size
					SCREAMING_SNAKE_CASE:Tuple   =   num_channels
					SCREAMING_SNAKE_CASE:Any   =   num_frames
					SCREAMING_SNAKE_CASE:List[Any]   =   image_size
					SCREAMING_SNAKE_CASE:List[Any]   =   min_resolution
					SCREAMING_SNAKE_CASE:int   =   max_resolution
					SCREAMING_SNAKE_CASE:Optional[Any]   =   do_resize
					SCREAMING_SNAKE_CASE:Optional[Any]   =   size
					SCREAMING_SNAKE_CASE:int   =   do_normalize
					SCREAMING_SNAKE_CASE:Any   =   image_mean
					SCREAMING_SNAKE_CASE:Optional[Any]   =   image_std
					SCREAMING_SNAKE_CASE:int   =   do_center_crop
					SCREAMING_SNAKE_CASE:int   =   crop_size







			def       __UpperCamelCase  (	self     : List[str]							):
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}





@require_torch
@require_vision
class 							_snake_case	(    UpperCAmelCase__		,					unittest.TestCase  ):
			_A    :       int      				=   TvltImageProcessor if is_vision_available() else None
			def       __UpperCamelCase  (	self     : List[str]							):
					SCREAMING_SNAKE_CASE:List[str]   =   TvltImageProcessorTester(self							)
			@property
			def       __UpperCamelCase  (	self     : Any							):
					return self.image_processor_tester.prepare_image_processor_dict()
			def       __UpperCamelCase  (	self     : str							):
					SCREAMING_SNAKE_CASE:List[Any]   =   self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(__lowercase       ,"image_mean"							)							)
					self.assertTrue(hasattr(__lowercase       ,"image_std"							)							)
					self.assertTrue(hasattr(__lowercase       ,"do_normalize"							)							)
					self.assertTrue(hasattr(__lowercase       ,"do_resize"							)							)
					self.assertTrue(hasattr(__lowercase       ,"do_center_crop"							)							)
					self.assertTrue(hasattr(__lowercase       ,"size"							)							)
			def       __UpperCamelCase  (	self     : Any							):
					SCREAMING_SNAKE_CASE:Optional[Any]   =   self.image_processing_class(**self.image_processor_dict							)
					# create random PIL videos
					SCREAMING_SNAKE_CASE:Union[str, Any]   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=__lowercase							)
					for video in video_inputs:
							self.assertIsInstance(__lowercase       ,__lowercase							)
							self.assertIsInstance(video[0]       ,Image.Image							)

					# Test not batched input
					SCREAMING_SNAKE_CASE:str   =   image_processor(video_inputs[0]       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)

					# Test batched
					SCREAMING_SNAKE_CASE:Any   =   image_processor(__lowercase       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)
			def       __UpperCamelCase  (	self     : List[str]							):
					SCREAMING_SNAKE_CASE:List[str]   =   self.image_processing_class(**self.image_processor_dict							)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE:Union[str, Any]   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=__lowercase       ,numpify=__lowercase							)
					for video in video_inputs:
							self.assertIsInstance(__lowercase       ,__lowercase							)
							self.assertIsInstance(video[0]       ,np.ndarray							)

					# Test not batched input
					SCREAMING_SNAKE_CASE:int   =   image_processor(video_inputs[0]       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)

					# Test batched
					SCREAMING_SNAKE_CASE:str   =   image_processor(__lowercase       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)







			def       __UpperCamelCase  (	self     : int							):
					SCREAMING_SNAKE_CASE:Optional[int]   =   self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE:Union[str, Any]   =   prepare_video_inputs(self.image_processor_tester       ,equal_resolution=__lowercase       ,torchify=__lowercase							)
					for video in video_inputs:
							self.assertIsInstance(__lowercase       ,__lowercase							)
							self.assertIsInstance(video[0]       ,torch.Tensor							)

					# Test not batched input
					SCREAMING_SNAKE_CASE:Any   =   image_processor(video_inputs[0]       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)

					# Test batched
					SCREAMING_SNAKE_CASE:Dict   =   image_processor(__lowercase       ,return_tensors="pt"							).pixel_values
					self.assertEqual(
					    encoded_videos.shape       ,(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["height"],
					        self.image_processor_tester.crop_size["width"],
					    )       ,)









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_      =							logging.get_logger(__name__)

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

  if hf_congfig.use_weighted_layer_sum:
    SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Featurizer"]["weights"]

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


if __name__ == "__main__":
    A_      =							argparse.ArgumentParser()
    parser.add_argument(
        "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
    )
    parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
    parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
    parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
    A_      =							parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






