



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__lowerCAmelCase									=   {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowerCAmelCase									=   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Optional[int]:
		for attribute in key.split('.'  ):
				_a     :      Optional[int]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

		if weight_type is not None:
				_a     :      Optional[Any]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  ).shape
		else:
				_a     :      Union[str, Any]        =							hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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     :      Optional[Any]        =							value
		elif weight_type == "weight_g":
				_a     :      Optional[int]        =							value
		elif weight_type == "weight_v":
				_a     :      Optional[Any]        =							value
		elif weight_type == "bias":
				_a     :      List[Any]        =							value
		elif weight_type == "running_mean":
				_a     :      Optional[int]        =							value
		elif weight_type == "running_var":
				_a     :      Optional[int]        =							value
		elif weight_type == "num_batches_tracked":
				_a     :      Tuple        =							value
		elif weight_type == "inv_freq":
				_a     :      Dict        =							value
		else:
				_a     :      List[str]        =							value

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Dict:
		_a     :      Union[str, Any]        =							[]
		_a     :      Any        =							fairseq_model.state_dict()

		_a     :      Any        =							hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				_a     :      Union[str, Any]        =							False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			hf_model.config.feat_extract_norm == 'group'						,			)
						_a     :      int        =							True
				else:
						for key, mapped_key in MAPPING.items():
								_a     :      Any        =							'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
										_a     :      Any        =							True
										if "*" in mapped_key:
												_a     :      Optional[Any]        =							name.split(lowerCAmelCase_  )[0].split('.'  )[-2]
												_a     :      Optional[int]        =							mapped_key.replace('*'						,			lowerCAmelCase_  )
										if "pos_bias_u" in name:
												_a     :      Any        =							None
										elif "pos_bias_v" in name:
												_a     :      Union[str, Any]        =							None
										elif "weight_g" in name:
												_a     :      Tuple        =							'weight_g'
										elif "weight_v" in name:
												_a     :      Dict        =							'weight_v'
										elif "bias" in name:
												_a     :      Optional[Any]        =							'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_a     :      Dict        =							'weight'
										elif "running_mean" in name:
												_a     :      str        =							'running_mean'
										elif "inv_freq" in name:
												_a     :      Optional[Any]        =							'inv_freq'
										elif "running_var" in name:
												_a     :      Tuple        =							'running_var'
										elif "num_batches_tracked" in name:
												_a     :      List[str]        =							'num_batches_tracked'
										else:
												_a     :      Optional[Any]        =							None
										set_recursively(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase_  )

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      Any        =							full_name.split('conv_layers.'  )[-1]
		_a     :      str        =							name.split('.'  )
		_a     :      Union[str, Any]        =							int(items[0]  )
		_a     :      Optional[Any]        =							int(items[1]  )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
						_a     :      Tuple        =							value
						logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
						_a     :      Optional[Any]        =							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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""  )
						_a     :      List[str]        =							value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f"""{full_name} has size {value.shape}, but"""
								    f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""  )
						_a     :      List[Any]        =							value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCAmelCase_  )



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=True  )			->      Optional[int]:
		if config_path is not None:
				_a     :      str        =							WavaVecaConformerConfig.from_pretrained(lowerCAmelCase_						,			hidden_act='swish'  )
		else:
				_a     :      List[str]        =							WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				_a     :      Dict        =							'rotary'

		if is_finetuned:
				if dict_path:
						_a     :      List[Any]        =							Dictionary.load(lowerCAmelCase_  )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_a     :      str        =							target_dict.pad_index
						_a     :      int        =							target_dict.bos_index
						_a     :      Optional[Any]        =							target_dict.eos_index
						_a     :      str        =							len(target_dict.symbols  )
						_a     :      List[Any]        =							os.path.join(lowerCAmelCase_						,			'vocab.json'  )
						if not os.path.isdir(lowerCAmelCase_  ):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase_  )  )
								return
						os.makedirs(lowerCAmelCase_						,			exist_ok=lowerCAmelCase_  )
						_a     :      List[Any]        =							target_dict.indices

						# fairseq has the <pad> and <s> switched
						_a     :      Union[str, Any]        =							0
						_a     :      Any        =							1
						with open(lowerCAmelCase_						,			'w'						,			encoding='utf-8'  ) as vocab_handle:
								json.dump(lowerCAmelCase_						,			lowerCAmelCase_  )
						_a     :      Union[str, Any]        =							WavaVecaCTCTokenizer(
						    lowerCAmelCase_						,			unk_token=target_dict.unk_word						,			pad_token=target_dict.pad_word						,			bos_token=target_dict.bos_word						,			eos_token=target_dict.eos_word						,			word_delimiter_token='|'						,			do_lower_case=lowerCAmelCase_						,			)
						_a     :      int        =							True if config.feat_extract_norm == 'layer' else False
						_a     :      Dict        =							WavaVecaFeatureExtractor(
						    feature_size=1						,			sampling_rate=16000						,			padding_value=0						,			do_normalize=lowerCAmelCase_						,			return_attention_mask=lowerCAmelCase_						,			)
						_a     :      str        =							WavaVecaProcessor(feature_extractor=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_  )
						processor.save_pretrained(lowerCAmelCase_  )

				_a     :      List[Any]        =							WavaVecaConformerForCTC(lowerCAmelCase_  )
		else:
				_a     :      Optional[int]        =							WavaVecaConformerForPreTraining(lowerCAmelCase_  )

		if is_finetuned:
				_a       ,					_a       ,					_a     :      List[Any]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,			arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
		else:
				_a     :      Dict        =							argparse.Namespace(task='audio_pretraining'  )
				_a     :      Any        =							fairseq.tasks.setup_task(lowerCAmelCase_  )

				_a       ,					_a       ,					_a     :      Union[str, Any]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,			task=lowerCAmelCase_  )

		_a     :      int        =							model[0].eval()

		recursively_load_weights(lowerCAmelCase_						,			lowerCAmelCase_						,			not is_finetuned  )

		hf_wavavec.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							parser.add_argument(
							    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
							)
							__lowerCAmelCase									=   parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)








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, prepare_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



				def 		__magic_name__(       self			:Tuple	)		->			Any:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return self.image_processor_tester.prepare_image_processor_dict()


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_mean'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''image_std'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_normalize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_resize'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''do_center_crop'''	)	)
										self.assertTrue(hasattr(lowerCAmelCase__			,			'''size'''	)	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 18}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 18, '''width''': 18}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.image_processing_class.from_dict(self.image_processor_dict			,			size=42			,			crop_size=84	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 42}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 84, '''width''': 84}	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			Image.Image	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(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						:     Optional[Any]					=		image_processing(lowerCAmelCase__			,			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 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     List[str]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			numpify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			np.ndarray	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(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_processing(lowerCAmelCase__			,			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 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		prepare_video_inputs(self.image_processor_tester			,			equal_resolution=lowerCAmelCase__			,			torchify=lowerCAmelCase__	)
										for video in video_inputs:
																self.assertIsInstance(lowerCAmelCase__			,			lowerCAmelCase__	)
																self.assertIsInstance(video[0]			,			torch.Tensor	)

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(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						:     Optional[int]					=		image_processing(lowerCAmelCase__			,			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'''],
										    )			,			)
