






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

__A          = {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'mask_emb': 'masked_spec_embed',
}




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					for attribute in key.split('''.'''       ):
										_A									=       getattr(_lowercase    ,	_lowercase       )

					if weight_type is not None:
										_A									=       getattr(_lowercase    ,	_lowercase       ).shape
					else:
										_A									=       hf_pointer.shape

					assert hf_shape == value.shape, (
					    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
					    f""" {value.shape} for {full_name}"""
					)

					if weight_type == "weight":
										_A									=       value
					elif weight_type == "weight_g":
										_A									=       value
					elif weight_type == "weight_v":
										_A									=       value
					elif weight_type == "bias":
										_A									=       value
					else:
										_A									=       value

					logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       []
					_A									=       fairseq_model.state_dict()

					_A									=       hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_A									=       False
										if "conv_layers" in name:
															load_conv_layer(
															    _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	hf_model.config.feat_extract_norm == '''group'''    ,	)
															_A									=       True
										else:
															for key, mapped_key in MAPPING.items():
																				_A									=       mapped_key

																				if key in name:
																									_A									=       True
																									if "*" in mapped_key:
																														_A									=       name.split(_lowercase       )[0].split('''.'''       )[-2]
																														_A									=       mapped_key.replace('''*'''    ,	_lowercase       )
																									if "weight_g" in name:
																														_A									=       '''weight_g'''
																									elif "weight_v" in name:
																														_A									=       '''weight_v'''
																									elif "weight" in name:
																														_A									=       '''weight'''
																									elif "bias" in name:
																														_A									=       '''bias'''
																									else:
																														_A									=       None
																									set_recursively(_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       )
																				continue
										if not is_used:
															unused_weights.append(_lowercase       )

					logger.warning(f"""Unused weights: {unused_weights}"""       )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







					_A									=       full_name.split('''conv_layers.'''       )[-1]
					_A									=       name.split('''.'''       )
					_A									=       int(items[0]       )
					_A									=       int(items[1]       )

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
															)
															_A									=       value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
															)
															_A									=       value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
															    " found."
															)
															_A									=       value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
															)
															_A									=       value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
					else:
										unused_weights.append(_lowercase       )




def    __A    (   _lowercase       ):

					'''simple docstring'''







					_A									=       HubertConfig()
					_A									=       model.config

					_A									=       fs_config.activation_dropout
					_A									=       False
					_A									=       fs_config.attention_dropout
					_A									=       False
					_A									=       eval(fs_config.extractor_conv_feature_layers       )
					_A									=       [x[0] for x in conv_layers]
					_A									=       [x[1] for x in conv_layers]
					_A									=       [x[2] for x in conv_layers]
					_A									=       '''gelu'''
					_A									=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					_A									=       False
					_A									=       0.0
					_A									=       0.0
					_A									=       fs_config.activation_fn
					_A									=       fs_config.dropout
					_A									=       fs_config.encoder_embed_dim
					_A									=       0.02
					_A									=       fs_config.encoder_ffn_embed_dim
					_A									=       1e-5
					_A									=       0.0
					_A									=       fs_config.encoder_attention_heads
					_A									=       fs_config.conv_pos_groups
					_A									=       fs_config.conv_pos
					_A									=       len(_lowercase       )
					_A									=       fs_config.encoder_layers

					return config




@torch.no_grad()
def    __A    (   _lowercase    ,	_lowercase=None       ):

					'''simple docstring'''







					_A									=       distilhubert().model.model

					if config_path is not None:
										_A									=       HubertConfig.from_pretrained(_lowercase       )
					else:
										_A									=       convert_config(_lowercase       )
					_A									=       model.eval()

					_A									=       WavaVecaFeatureExtractor(
					    feature_size=1    ,	sampling_rate=1_60_00    ,	padding_value=0    ,	do_normalize=_lowercase    ,	return_attention_mask=_lowercase    ,	)
					_A									=       HubertModel(_lowercase       )

					recursively_load_weights(_lowercase    ,	_lowercase       )

					feature_extractor.save_pretrained(_lowercase       )
					hf_model.save_pretrained(_lowercase       )


if __name__ == "__main__":
				__A          = argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				__A          = parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)









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 					snake_case					(							A						,   A=10						,   A=10						,   A=False						,   A=False			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		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
		lowerCamelCase_							:		Any							   =							[Image.fromarray(np.moveaxis(A						,   0						,   -1			)			) for frame in video]

	if torchify:
		lowerCamelCase_							:		List[str]							   =							[torch.from_numpy(A			) for frame in video]

	return video






def 					snake_case					(							A						,   A=False						,   A=False						,   A=False			)							->							Dict:




	'''simple docstring'''


	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	lowerCamelCase_							:		int							   =							[]
	for i in range(image_processor_tester.batch_size			):
		if equal_resolution:
			lowerCamelCase_							:		Union[str, Any]							   =							image_processor_tester.max_resolution
		else:
			lowerCamelCase_   ,			lowerCamelCase_							:		Optional[int]							   =							np.random.choice(
			    np.arange(image_processor_tester.min_resolution						,   image_processor_tester.max_resolution			)						,   2			)
			lowerCamelCase_							:		str							   =							prepare_video(
			    image_processor_tester=A						,   width=A						,   height=A						,   numpify=A						,   torchify=A						,   )
		video_inputs.append(A			)

	return video_inputs


class 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=7 ,       __lowercase=3 ,       __lowercase=4 ,       __lowercase=18 ,       __lowercase=30 ,       __lowercase=400 ,       __lowercase=True ,       __lowercase=None ,       __lowercase=True ,       __lowercase=[0.5, 0.5, 0.5] ,       __lowercase=[0.5, 0.5, 0.5] ,       __lowercase=True ,       __lowercase=None ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							   =							size if size is not None else {'shortest_edge': 18}
						lowerCamelCase_							:		Union[str, Any]							   =							crop_size if crop_size is not None else {'height': 18, 'width': 18}

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





					def 	lowercase_							(				self					):







						'''simple docstring'''




						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 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = TvltImageProcessor if is_vision_available() else None

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							TvltImageProcessorTester(self					)

					@property
					def 	lowercase_							(				self					):







						'''simple docstring'''




						return self.image_processor_tester.prepare_image_processor_dict()

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random PIL videos
						lowerCamelCase_							:		Dict							   =							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
						lowerCamelCase_							:		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
						lowerCamelCase_							:		List[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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random numpy tensors
						lowerCamelCase_							:		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
						lowerCamelCase_							:		Union[str, 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
						lowerCamelCase_							:		Tuple							   =							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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							self.image_processing_class(**self.image_processor_dict					)
						# create random PyTorch tensors
						lowerCamelCase_							:		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
						lowerCamelCase_							:		Optional[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
						lowerCamelCase_							:		Tuple							   =							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'],
						    ) ,       )
