

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_							:					List[Any]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					Optional[int]		 =   {
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   int			,a__    :   Union[str, Any]			,a__    :   Union[str, Any]       )					->				str:
	for attribute in key.split("""."""       ):
		__A :			Union[str, Any]		    =			getattr(a__			,a__       )

	if weight_type is not None:
		__A :			Optional[int]		    =			getattr(a__			,a__       ).shape
	else:
		__A :			List[Any]		    =			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 :			Optional[Any]		    =			value
	elif weight_type == "weight_g":
		__A :			List[Any]		    =			value
	elif weight_type == "weight_v":
		__A :			Optional[Any]		    =			value
	elif weight_type == "bias":
		__A :			Dict		    =			value
	else:
		__A :			Any		    =			value

	logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   List[Any]			,a__    :   str       )					->				Optional[Any]:
	__A :			List[Any]		    =			[]
	__A :			str		    =			fairseq_model.state_dict()

	__A :			List[Any]		    =			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__A :			Any		    =			False
		if "conv_layers" in name:
			load_conv_layer(
			    a__			,a__			,a__			,a__			,hf_model.config.feat_extract_norm == """group"""			,)
			__A :			int		    =			True
		else:
			for key, mapped_key in MAPPING.items():
				__A :			Union[str, Any]		    =			"""hubert.""" + 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] and not is_finetuned):
					__A :			int		    =			True
					if "*" in mapped_key:
						__A :			List[Any]		    =			name.split(a__       )[0].split("""."""       )[-2]
						__A :			int		    =			mapped_key.replace("""*"""			,a__       )
					if "weight_g" in name:
						__A :			Tuple		    =			"""weight_g"""
					elif "weight_v" in name:
						__A :			Dict		    =			"""weight_v"""
					elif "weight" in name:
						__A :			Any		    =			"""weight"""
					elif "bias" in name:
						__A :			Optional[Any]		    =			"""bias"""
					else:
						__A :			Union[str, Any]		    =			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 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]			,a__    :   str			,a__    :   List[Any]       )					->				Union[str, Any]:
	__A :			Any		    =			full_name.split("""conv_layers."""       )[-1]
	__A :			List[str]		    =			name.split("""."""       )
	__A :			Optional[int]		    =			int(items[0]       )
	__A :			List[str]		    =			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 :			Union[str, Any]		    =			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 :			int		    =			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 :			Union[str, Any]		    =			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 :			int		    =			value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
	else:
		unused_weights.append(a__       )
@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Tuple			,a__    :   Dict=None			,a__    :   str=None			,a__    :   Optional[Any]=True       )					->				int:
	if config_path is not None:
		__A :			str		    =			HubertConfig.from_pretrained(a__       )
	else:
		__A :			List[str]		    =			HubertConfig()

	if is_finetuned:
		if dict_path:
			__A :			Tuple		    =			Dictionary.load(a__       )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			__A :			List[str]		    =			target_dict.pad_index
			__A :			int		    =			target_dict.bos_index
			__A :			List[str]		    =			target_dict.eos_index
			__A :			Optional[int]		    =			len(target_dict.symbols       )
			__A :			str		    =			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 :			int		    =			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 :			Dict		    =			True if config.feat_extract_norm == """layer""" else False
			__A :			Optional[int]		    =			WavaVecaFeatureExtractor(
			    feature_size=1			,sampling_rate=16000			,padding_value=0			,do_normalize=a__			,return_attention_mask=a__			,)
			__A :			Optional[Any]		    =			WavaVecaProcessor(feature_extractor=a__			,tokenizer=a__       )
			processor.save_pretrained(a__       )

		__A :			Dict		    =			HubertForCTC(a__       )
	else:
		__A :			Optional[Any]		    =			HubertModel(a__       )

	if is_finetuned:
		__A    ,				__A    ,				__A :			Optional[Any]		    =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]			,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
	else:
		__A    ,				__A    ,				__A :			Optional[int]		    =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

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

	recursively_load_weights(a__			,a__			,a__       )

	hf_wavavec.save_pretrained(a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					str		 =   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'''
			)
			UpperCAmelCase_							:					Optional[int]		 =   parser.parse_args()
			convert_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)






'''simple docstring'''




import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm
						import torch_xla.debug.metrics as met






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __init__(  self			,       *__lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       **__lowerCAmelCase):




					"""simple docstring"""


					super().__init__(*__lowerCAmelCase			,       **__lowerCAmelCase)
					lowerCAmelCase     =    eval_examples
					lowerCAmelCase     =    post_process_function



			def 				a_			(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "eval"):




					"""simple docstring"""


					lowerCAmelCase     =    self.eval_dataset if eval_dataset is None else eval_dataset
					lowerCAmelCase     =    self.get_eval_dataloader(__lowerCAmelCase)
					lowerCAmelCase     =    self.eval_examples if eval_examples is None else eval_examples

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Evaluation"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))
					if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
							# Only the main node write the results by default
							lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions)
							lowerCAmelCase     =    self.compute_metrics(__lowerCAmelCase)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()):
									if not key.startswith(f"{metric_key_prefix}_"):
											lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
							metrics.update(output.metrics)
					else:
							lowerCAmelCase     =    output.metrics

					if self.args.should_log:
							# Only the main node log the results by default
							self.log(__lowerCAmelCase)

					if self.args.tpu_metrics_debug or self.args.debug:
							# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
							xm.master_print(met.metrics_report())

					lowerCAmelCase     =    self.callback_handler.on_evaluate(self.args			,       self.state			,       self.control			,       __lowerCAmelCase)
					return metrics







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase = "test"):




					"""simple docstring"""


					lowerCAmelCase     =    self.get_test_dataloader(__lowerCAmelCase)

					# Temporarily disable metric computation, we will do it in the loop here.
					lowerCAmelCase     =    self.compute_metrics
					lowerCAmelCase     =    None
					lowerCAmelCase     =    self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
					lowerCAmelCase     =    time.time()
					try:
							lowerCAmelCase     =    eval_loop(
							    __lowerCAmelCase			,       description="""Prediction"""			,       prediction_loss_only=True if compute_metrics is None else None			,       ignore_keys=__lowerCAmelCase			,       metric_key_prefix=__lowerCAmelCase			,       )
					finally:
							lowerCAmelCase     =    compute_metrics
					lowerCAmelCase     =    self.args.eval_batch_size * self.args.world_size
					if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
							start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
					output.metrics.update(
					    speed_metrics(
					        __lowerCAmelCase			,       __lowerCAmelCase			,       num_samples=output.num_samples			,       num_steps=math.ceil(output.num_samples / total_batch_size)			,       ))

					if self.post_process_function is None or self.compute_metrics is None:
							return output

					lowerCAmelCase     =    self.post_process_function(__lowerCAmelCase			,       __lowerCAmelCase			,       output.predictions			,       """predict""")
					lowerCAmelCase     =    self.compute_metrics(__lowerCAmelCase)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()):
							if not key.startswith(f"{metric_key_prefix}_"):
									lowerCAmelCase     =    metrics.pop(__lowerCAmelCase)
					metrics.update(output.metrics)
					return PredictionOutput(predictions=predictions.predictions			,       label_ids=predictions.label_ids			,       metrics=__lowerCAmelCase)




