






"""simple docstring"""





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()
__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    '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',
}
__lowerCamelCase							=  [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]



def   a    (     __UpperCAmelCase      :    Optional[Any]  )    ->							Optional[int]:
						__magic_name__:	Optional[Any]							=       {}
						with open(__UpperCAmelCase      ,   """r"""  ) as file:
												for line_number, line in enumerate(__UpperCAmelCase  ):
																		__magic_name__:	Dict							=       line.strip()
																		if line:
																								__magic_name__:	str							=       line.split()
																								__magic_name__:	Tuple							=       line_number
																								__magic_name__:	Tuple							=       words[0]
																								__magic_name__:	Tuple							=       value
						return result



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    List[str]  )    ->							Tuple:
						for attribute in key.split("""."""  ):
												__magic_name__:	Dict							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )

						__magic_name__:	Any							=       None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__UpperCAmelCase  ):
																		__magic_name__:	Any							=       PARAM_MAPPING[full_name.split("""."""  )[-1]]
																		__magic_name__:	Optional[int]							=       """param"""

						if weight_type is not None and weight_type != "param":
												__magic_name__:	Tuple							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  ).shape
						elif weight_type is not None and weight_type == "param":
												__magic_name__:	str							=       hf_pointer
												for attribute in hf_param_name.split("""."""  ):
																		__magic_name__:	List[Any]							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	int							=       shape_pointer.shape

												# let's reduce dimension
												__magic_name__:	Any							=       value[0]
						else:
												__magic_name__:	Dict							=       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":
												__magic_name__:	Tuple							=       value
						elif weight_type == "weight_g":
												__magic_name__:	List[Any]							=       value
						elif weight_type == "weight_v":
												__magic_name__:	int							=       value
						elif weight_type == "bias":
												__magic_name__:	Optional[Any]							=       value
						elif weight_type == "param":
												for attribute in hf_param_name.split("""."""  ):
																		__magic_name__:	Union[str, Any]							=       getattr(__UpperCAmelCase      ,   __UpperCAmelCase  )
												__magic_name__:	Dict							=       value
						else:
												__magic_name__:	Optional[int]							=       value

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



def   a    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    List[str]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Optional[Any]  )    ->							Optional[int]:
						__magic_name__:	int							=       None
						for param_key in PARAM_MAPPING.keys():
												if full_name.endswith(__UpperCAmelCase  ):
																		__magic_name__:	Dict							=       PARAM_MAPPING[full_name.split("""."""  )[-1]]
																		__magic_name__:	List[str]							=       """param"""

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

						__magic_name__:	Optional[int]							=       value if """lm_head""" in full_key else value[0]


__lowerCamelCase							=  {
    '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    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Optional[int]=None      ,   __UpperCAmelCase      :    Union[str, Any]=None  )    ->							Optional[Any]:
						__magic_name__:	Tuple							=       False
						for key, mapped_key in MAPPING.items():
												__magic_name__:	Any							=       """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]:
																		__magic_name__:	Tuple							=       True
																		if "*" in mapped_key:
																								__magic_name__:	Optional[Any]							=       name.split(__UpperCAmelCase  )[0].split("""."""  )[-2]
																								__magic_name__:	Tuple							=       mapped_key.replace("""*"""      ,   __UpperCAmelCase  )
																		if "weight_g" in name:
																								__magic_name__:	Optional[Any]							=       """weight_g"""
																		elif "weight_v" in name:
																								__magic_name__:	Optional[int]							=       """weight_v"""
																		elif "bias" in name:
																								__magic_name__:	Tuple							=       """bias"""
																		elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								__magic_name__:	Union[str, Any]							=       """weight"""
																		else:
																								__magic_name__:	Optional[int]							=       None
																		if hf_dict is not None:
																								rename_dict(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																		else:
																								set_recursively(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																		return is_used
						return is_used



def   a    (     __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    Any  )    ->							Dict:
						__magic_name__:	List[str]							=       []
						__magic_name__:	int							=       fairseq_model.state_dict()

						__magic_name__:	Tuple							=       hf_model.wavaveca.feature_extractor

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

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



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    Any      ,   __UpperCAmelCase      :    Union[str, Any]  )    ->							List[str]:
						__magic_name__:	List[str]							=       full_name.split("""conv_layers."""  )[-1]
						__magic_name__:	List[Any]							=       name.split("""."""  )
						__magic_name__:	int							=       int(items[0]  )
						__magic_name__:	List[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.'  )
																		__magic_name__:	Union[str, Any]							=       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.'  )
																		__magic_name__:	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.'  )
																		__magic_name__:	Optional[Any]							=       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.'  )
																		__magic_name__:	str							=       value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'  )
						else:
												unused_weights.append(__UpperCAmelCase  )



@torch.no_grad()
def   a    (     __UpperCAmelCase      :    Optional[int]      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    Union[str, Any]=None      ,   __UpperCAmelCase      :    Any=None      ,   __UpperCAmelCase      :    Tuple=True      ,   __UpperCAmelCase      :    List[Any]=False  )    ->							Any:
						if config_path is not None:
												__magic_name__:	Tuple							=       WavaVecaConfig.from_pretrained(__UpperCAmelCase  )
						else:
												__magic_name__:	int							=       WavaVecaConfig()

						if is_seq_class:
												__magic_name__:	Optional[int]							=       read_txt_into_dict(__UpperCAmelCase  )
												__magic_name__:	Any							=       idalabel
												__magic_name__:	Tuple							=       WavaVecaForSequenceClassification(__UpperCAmelCase  )
												__magic_name__:	Tuple							=       WavaVecaFeatureExtractor(
												    feature_size=1      ,   sampling_rate=1_6_0_0_0      ,   padding_value=0      ,   do_normalize=__UpperCAmelCase      ,   return_attention_mask=__UpperCAmelCase      ,   )
												feature_extractor.save_pretrained(__UpperCAmelCase  )

						elif is_finetuned:
												if dict_path:
																		__magic_name__:	Optional[int]							=       Dictionary.load(__UpperCAmelCase  )

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

																		# fairseq has the <pad> and <s> switched
																		__magic_name__:	List[str]							=       0
																		__magic_name__:	Union[str, Any]							=       1
																		with open(__UpperCAmelCase      ,   """w"""      ,   encoding="""utf-8"""  ) as vocab_handle:
																								json.dump(__UpperCAmelCase      ,   __UpperCAmelCase  )
																		__magic_name__:	Optional[Any]							=       WavaVecaCTCTokenizer(
																		    __UpperCAmelCase      ,   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=__UpperCAmelCase      ,   )
																		__magic_name__:	Optional[Any]							=       True if config.feat_extract_norm == """layer""" else False
																		__magic_name__:	Optional[Any]							=       WavaVecaFeatureExtractor(
																		    feature_size=1      ,   sampling_rate=1_6_0_0_0      ,   padding_value=0      ,   do_normalize=__UpperCAmelCase      ,   return_attention_mask=__UpperCAmelCase      ,   )
																		__magic_name__:	List[Any]							=       WavaVecaProcessor(feature_extractor=__UpperCAmelCase      ,   tokenizer=__UpperCAmelCase  )
																		processor.save_pretrained(__UpperCAmelCase  )

												__magic_name__:	List[Any]							=       WavaVecaForCTC(__UpperCAmelCase  )
						else:
												__magic_name__:	Dict							=       WavaVecaForPreTraining(__UpperCAmelCase  )

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

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

						__magic_name__:	List[Any]							=       model[0].eval()

						recursively_load_weights(__UpperCAmelCase      ,   __UpperCAmelCase      ,   not is_finetuned  )

						hf_wavavec.save_pretrained(__UpperCAmelCase  )


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'
							)
							parser.add_argument(
							    '--is_seq_class',
							    action='store_true',
							    help='Whether the model to convert is a fine-tuned sequence classification model or not',
							)
							__lowerCamelCase							=  parser.parse_args()

							__lowerCamelCase							=  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,
							)





"""simple docstring"""


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
						import optax
						from flax.training.common_utils import onehot

						from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
						from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  __lowerCamelCase  ( unittest.TestCase			):




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
							lowerCamelCase_					=  AutoTokenizer.from_pretrained('''google/mt5-small'''   )

							lowerCamelCase_					=  tokenizer('''Hello there'''							,						return_tensors='''np'''   ).input_ids
							lowerCamelCase_					=  tokenizer('''Hi I am'''							,						return_tensors='''np'''   ).input_ids

							lowerCamelCase_					=  shift_tokens_right(UpperCAmelCase							,						model.config.pad_token_id							,						model.config.decoder_start_token_id   )

							lowerCamelCase_					=  model(UpperCAmelCase							,						decoder_input_ids=UpperCAmelCase   ).logits
							lowerCamelCase_					=  optax.softmax_cross_entropy(UpperCAmelCase							,						onehot(UpperCAmelCase							,						logits.shape[-1]   )   ).mean()

							lowerCamelCase_					=  -(labels.shape[-1] * loss.item())

							lowerCamelCase_					=  -8_4.9_1_2_7
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )



