






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_           = {
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}







def      snake_case		(							UpperCAmelCase	:       str,						UpperCAmelCase	:       str,						UpperCAmelCase	:       str,						UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any]   ):



				for attribute in key.split('.'   ):
								A	 =      getattr(A__,						A__   )

				if weight_type is not None:
								A	 =      getattr(A__,						A__   ).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      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[str]   ):
				A	 =      []
				A	 =      fairseq_model.state_dict()

				A	 =      hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								A	 =      False
								if "conv_layers" in name:
												load_conv_layer(
												    A__,						A__,						A__,						A__,						hf_model.config.feat_extract_norm == 'group',						)
												A	 =      True
								else:
												for key, mapped_key in MAPPING.items():
																A	 =      'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																				A	 =      True
																				if "*" in mapped_key:
																								A	 =      name.split(A__   )[0].split('.'   )[-2]
																								A	 =      mapped_key.replace('*',						A__   )
																				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(A__,						A__,						A__,						A__,						A__   )
																continue
								if not is_used:
												unused_weights.append(A__   )

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







def      snake_case		(							UpperCAmelCase	:       Dict,						UpperCAmelCase	:       int,						UpperCAmelCase	:       Dict,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Optional[Any]   ):
				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(A__   )







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Optional[int]   ):
				A	 =      SEWConfig()
				if is_finetuned:
								A	 =      model.wav_encoder.wav_model.cfg
				else:
								A	 =      model.cfg

				A	 =      fs_config.conv_bias
				A	 =      eval(fs_config.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	 =      0.0
				A	 =      fs_config.activation_fn.name
				A	 =      fs_config.encoder_embed_dim
				A	 =      0.02
				A	 =      fs_config.encoder_ffn_embed_dim
				A	 =      1E-5
				A	 =      fs_config.encoder_layerdrop
				A	 =      fs_config.encoder_attention_heads
				A	 =      fs_config.conv_pos_groups
				A	 =      fs_config.conv_pos
				A	 =      len(A__   )
				A	 =      fs_config.encoder_layers
				A	 =      fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								A	 =      model.cfg
								A	 =      fs_config.final_dropout
								A	 =      fs_config.layerdrop
				A	 =      fs_config.activation_dropout
				A	 =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				A	 =      fs_config.attention_dropout
				A	 =      fs_config.dropout_input
				A	 =      fs_config.dropout
				A	 =      fs_config.mask_channel_length
				A	 =      fs_config.mask_channel_prob
				A	 =      fs_config.mask_length
				A	 =      fs_config.mask_prob

				A	 =      'Wav2Vec2FeatureExtractor'
				A	 =      'Wav2Vec2CTCTokenizer'

				return config







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       int,						UpperCAmelCase	:       Any=None,						UpperCAmelCase	:       str=None,						UpperCAmelCase	:       Optional[int]=True   ):



				if is_finetuned:
								A ,		A ,		A	 =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path],						arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
				else:
								A ,		A ,		A	 =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

				if config_path is not None:
								A	 =      SEWConfig.from_pretrained(A__   )
				else:
								A	 =      convert_config(model[0],						A__   )
				A	 =      model[0].eval()

				A	 =      True if config.feat_extract_norm == 'layer' else False
				A	 =      WavaVecaFeatureExtractor(
				    feature_size=1,						sampling_rate=1_60_00,						padding_value=0,						do_normalize=A__,						return_attention_mask=A__,						)

				if is_finetuned:
								if dict_path:
												A	 =      Dictionary.load(A__   )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												A	 =      target_dict.pad_index
												A	 =      target_dict.bos_index
												A	 =      target_dict.pad_index
												A	 =      target_dict.bos_index
												A	 =      target_dict.eos_index
												A	 =      len(target_dict.symbols   )
												A	 =      os.path.join(A__,						'vocab.json'   )
												if not os.path.isdir(A__   ):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A__   )   )
																return
												os.makedirs(A__,						exist_ok=A__   )
												with open(A__,						'w',						encoding='utf-8'   ) as vocab_handle:
																json.dump(target_dict.indices,						A__   )
												A	 =      WavaVecaCTCTokenizer(
												    A__,						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=A__,						)
												A	 =      WavaVecaProcessor(feature_extractor=A__,						tokenizer=A__   )
												processor.save_pretrained(A__   )

								A	 =      SEWForCTC(A__   )
				else:
								A	 =      SEWModel(A__   )
								feature_extractor.save_pretrained(A__   )

				recursively_load_weights(A__,						A__,						A__   )

				hf_model.save_pretrained(A__   )


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(
					    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)








import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		snake_case    =   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE						,feature_extractor=_SCREAMING_SNAKE_CASE		)

						# test with a raw waveform
						A	 =      np.zeros((3_4_0_0_0,)		)
						A	 =      np.zeros((1_4_0_0_0,)		)
						return audio_classifier, [audioa, audio]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A ,		A	 =      examples
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						# by default a model is initialized with num_labels=2
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=1		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)

						self.run_torchaudio(_SCREAMING_SNAKE_CASE		)
		@require_torchaudio
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						import datasets

						# test with a local file
						A	 =      datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'						,'clean'						,split='validation'		)
						A	 =      dataset[0]['audio']['array']
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
		@require_torch
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      'anton-l/wav2vec2-random-tiny-classifier'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)

						A	 =      np.ones((8_0_0_0,)		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)

						A	 =      [
						    {'score': 0.0842, 'label': 'no'},
						    {'score': 0.0838, 'label': 'up'},
						    {'score': 0.0837, 'label': 'go'},
						    {'score': 0.0834, 'label': 'right'},
						]
						A	 =      [
						    {'score': 0.0845, 'label': 'stop'},
						    {'score': 0.0844, 'label': 'on'},
						    {'score': 0.0841, 'label': 'right'},
						    {'score': 0.0834, 'label': 'left'},
						]
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)

						A	 =      {'array': np.ones((8_0_0_0,)		), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)
		@require_torch
		@slow
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						import datasets

						A	 =      'superb/wav2vec2-base-superb-ks'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)
						A	 =      datasets.load_dataset('anton-l/superb_dummy'						,'ks'						,split='test'		)

						A	 =      np.array(dataset[3]['speech']						,dtype=np.floataa		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertEqual(
						    nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=3		)						,[
						        {'score': 0.981, 'label': 'go'},
						        {'score': 0.007, 'label': 'up'},
						        {'score': 0.006, 'label': '_unknown_'},
						        {'score': 0.001, 'label': 'down'},
						    ]						,)







		@require_tf
		@unittest.skip('Audio classification is not implemented for TF'		)
		def       A(  self		:       Any		)    ->   int:


						'''simple docstring'''
						pass





