




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch

if is_datasets_available():
		from datasets import load_dataset

__UpperCamelCase					:       Optional[Any]    					=      random.Random()





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase=1.0							,			__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Tuple:
	if rng is None:
		a										=							global_rng

	a										=							[]
	for batch_idx in range(shape[0]    ):
		values.append([]    )
		for _ in range(shape[1]    ):
			values[-1].append(rng.random() * scale    )

	return values

class 		__lowerCAmelCase	(	unittest.TestCase ):
							def __init__(						self						:Tuple		,      __magic_name__						:Optional[int]		,      __magic_name__						:Any=7		,      __magic_name__						:Union[str, Any]=400		,      __magic_name__						:Optional[Any]=2000		,      __magic_name__						:int=2048		,      __magic_name__						:List[Any]=128		,      __magic_name__						:Dict=1		,      __magic_name__						:str=512		,      __magic_name__						:Union[str, Any]=30		,      __magic_name__						:Any=4_4100		,      ):







								'''simple docstring'''







								a										=							parent
								a										=							batch_size
								a										=							min_seq_length
								a										=							max_seq_length
								a										=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a										=							spectrogram_length
								a										=							feature_size
								a										=							num_audio_channels
								a										=							hop_length
								a										=							chunk_length
								a										=							sampling_rate
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}





							def       lowerCamelCase__							(						self						:Any		,      __magic_name__						:List[str]=False		,      __magic_name__						:Dict=False				):







								'''simple docstring'''







								def _flatten(__magic_name__						:List[Any]				):
									return list(itertools.chain(*__magic_name__				)				)

								if equal_length:
									a										=							[floats_list((self.max_seq_length, self.feature_size)				) for _ in range(self.batch_size				)]
								else:
									# make sure that inputs increase in size
									a										=							[
									    floats_list((x, self.feature_size)				)
									    for x in range(self.min_seq_length		,      self.max_seq_length		,      self.seq_length_diff				)
									]
								if numpify:
									a										=							[np.asarray(__magic_name__				) for x in speech_inputs]
								return speech_inputs







@require_torch
@require_torchaudio
class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  TvltFeatureExtractor
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							TvltFeatureExtractionTester(self				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							self.feature_extraction_class(**self.feat_extract_dict				)
								self.assertTrue(hasattr(__magic_name__		,      """spectrogram_length"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """feature_size"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """num_audio_channels"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """hop_length"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """chunk_length"""				)				)
								self.assertTrue(hasattr(__magic_name__		,      """sampling_rate"""				)				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							self.feature_extraction_class(**self.feat_extract_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
									a										=							feat_extract_first.save_pretrained(__magic_name__				)[0]
									check_json_file_has_correct_format(__magic_name__				)
									a										=							self.feature_extraction_class.from_pretrained(__magic_name__				)

								a										=							feat_extract_first.to_dict()
								a										=							feat_extract_second.to_dict()
								a										=							dict_first.pop("""mel_filters"""				)
								a										=							dict_second.pop("""mel_filters"""				)
								self.assertTrue(np.allclose(__magic_name__		,      __magic_name__				)				)
								self.assertEqual(__magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.feature_extraction_class(**self.feat_extract_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
									a										=							os.path.join(__magic_name__		,      """feat_extract.json"""				)
									feat_extract_first.to_json_file(__magic_name__				)
									a										=							self.feature_extraction_class.from_json_file(__magic_name__				)

								a										=							feat_extract_first.to_dict()
								a										=							feat_extract_second.to_dict()
								a										=							dict_first.pop("""mel_filters"""				)
								a										=							dict_second.pop("""mel_filters"""				)
								self.assertTrue(np.allclose(__magic_name__		,      __magic_name__				)				)
								self.assertEqual(__magic_name__		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								a										=							self.feature_extraction_class(**self.feat_extract_dict				)

								# create three inputs of length 800, 1000, and 1200
								a										=							[floats_list((1, x)				)[0] for x in range(800		,      1400		,      200				)]
								a										=							[np.asarray(__magic_name__				) for speech_input in speech_inputs]

								# Test not batched input
								a										=							feature_extractor(np_speech_inputs[0]		,      return_tensors="""np"""		,      sampling_rate=4_4100				).audio_values

								self.assertTrue(encoded_audios.ndim == 4				)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

								# Test batched
								a										=							feature_extractor(__magic_name__		,      return_tensors="""np"""		,      sampling_rate=4_4100				).audio_values

								self.assertTrue(encoded_audios.ndim == 4				)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

								# Test audio masking
								a										=							feature_extractor(
								    __magic_name__		,      return_tensors="""np"""		,      sampling_rate=4_4100		,      mask_audio=__magic_name__				).audio_values

								self.assertTrue(encoded_audios.ndim == 4				)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)

								# Test 2-D numpy arrays are batched.
								a										=							[floats_list((1, x)				)[0] for x in (800, 800, 800)]
								a										=							np.asarray(__magic_name__				)
								a										=							feature_extractor(__magic_name__		,      return_tensors="""np"""		,      sampling_rate=4_4100				).audio_values
								self.assertTrue(encoded_audios.ndim == 4				)
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size				)
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length				)
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels				)
							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:Tuple				):







								'''simple docstring'''







								a										=							load_dataset("""hf-internal-testing/librispeech_asr_dummy"""		,      """clean"""		,      split="""validation"""				)
								# automatic decoding with librispeech
								a										=							ds.sort("""id"""				).select(range(__magic_name__				)				)[:num_samples]["""audio"""]

								return [x["array"] for x in speech_samples]





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self._load_datasamples(1				)
								a										=							TvltFeatureExtractor()
								a										=							feature_extractor(__magic_name__		,      return_tensors="""pt"""				).audio_values

								self.assertEquals(audio_values.shape		,      (1, 1, 192, 128)				)

								a										=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]				)
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		,      __magic_name__		,      atol=1E-4				)				)







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__UpperCamelCase					:       Union[str, Any]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Any    					=      {
    "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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__UpperCamelCase					:       Dict    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 		__A		(	__lowerCamelCase    )      ->       List[str]:
	a										=							{}
	with open(__lowerCamelCase							,			"""r"""    ) as file:
		for line_number, line in enumerate(__lowerCamelCase    ):
			a										=							line.strip()
			if line:
				a										=							line.split()
				a										=							line_number
				a										=							words[0]
				a										=							value
	return result





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	elif weight_type is not None and weight_type == "param":
		a										=							hf_pointer
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		a										=							shape_pointer.shape

		# let's reduce dimension
		a										=							value[0]
	else:
		a										=							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										=							value
	elif weight_type == "weight_g":
		a										=							value
	elif weight_type == "weight_v":
		a										=							value
	elif weight_type == "bias":
		a										=							value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		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		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							""".""".join([key, weight_type]    )
	elif weight_type is not None and weight_type == "param":
		a										=							""".""".join([key, hf_param_name]    )
	else:
		a										=							key

	a										=							value if """lm_head""" in full_key else value[0]


__UpperCamelCase					:       List[Any]    					=      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Optional[Any]:
	a										=							False
	for key, mapped_key in MAPPING.items():
		a										=							"""wav2vec2.""" + 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										=							True
			if "*" in mapped_key:
				a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
				a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
			if "weight_g" in name:
				a										=							"""weight_g"""
			elif "weight_v" in name:
				a										=							"""weight_v"""
			elif "bias" in name:
				a										=							"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				a										=							"""weight"""
			else:
				a										=							None
			if hf_dict is not None:
				rename_dict(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			else:
				set_recursively(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			return is_used
	return is_used





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Any]:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		a										=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		else:
			a										=							load_wavaveca_layer(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
		if not is_used:
			unused_weights.append(__lowerCamelCase    )

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       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:
			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										=							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										=							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										=							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										=							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 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=True							,			__lowerCamelCase=False    )      ->       List[Any]:
	if config_path is not None:
		a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	else:
		a										=							WavaVecaConfig()

	if is_seq_class:
		a										=							read_txt_into_dict(__lowerCamelCase    )
		a										=							idalabel
		a										=							WavaVecaForSequenceClassification(__lowerCamelCase    )
		a										=							WavaVecaFeatureExtractor(
		    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
		feature_extractor.save_pretrained(__lowerCamelCase    )

	elif is_finetuned:
		if dict_path:
			a										=							Dictionary.load(__lowerCamelCase    )

			# 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.eos_index
			a										=							len(target_dict.symbols    )
			a										=							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										=							target_dict.indices

			# fairseq has the <pad> and <s> switched
			a										=							0
			a										=							1
			with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as vocab_handle:
				json.dump(__lowerCamelCase							,			__lowerCamelCase    )
			a										=							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										=							True if config.feat_extract_norm == """layer""" else False
			a										=							WavaVecaFeatureExtractor(
			    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
			a										=							WavaVecaProcessor(feature_extractor=__lowerCamelCase							,			tokenizer=__lowerCamelCase    )
			processor.save_pretrained(__lowerCamelCase    )

		a										=							WavaVecaForCTC(__lowerCamelCase    )
	else:
		a										=							WavaVecaForPreTraining(__lowerCamelCase    )

	if is_finetuned or is_seq_class:
		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
	else:
		a										=							argparse.Namespace(task="""audio_pretraining"""    )
		a										=							fairseq.tasks.setup_task(__lowerCamelCase    )

		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,			task=__lowerCamelCase    )

	a										=							model[0].eval()

	recursively_load_weights(__lowerCamelCase							,			__lowerCamelCase							,			not is_finetuned    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       int    					=      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"
		)
		parser.add_argument(
		    "--is_seq_class",
		    action="store_true",
		    help="Whether the model to convert is a fine-tuned sequence classification model or not",
		)
		__UpperCamelCase					:       Union[str, Any]    					=      parser.parse_args()

		__UpperCamelCase					:       Any    					=      not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)

