




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      :    Optional[int]   		=   logging.get_logger(__name__)

__lowerCamelCase      :    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""",
    """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      :    str   		=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]     ):
	snake_case__				:   Any     =						{}
	with open(snake_case_  ,						"r"     ) as file:
		for line_number, line in enumerate(snake_case_     ):
			snake_case__				:   List[str]     =						line.strip()
			if line:
				snake_case__				:   List[str]     =						line.split()
				snake_case__				:   Optional[int]     =						line_number
				snake_case__				:   Union[str, Any]     =						words[0]
				snake_case__				:   Optional[int]     =						value
	return result







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							Tuple  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							List[Any]     ):







	for attribute in key.split("."     ):
		snake_case__				:   Any     =						getattr(snake_case_  ,						snake_case_     )

	snake_case__				:   Optional[int]     =						None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(snake_case_     ):
			snake_case__				:   List[str]     =						PARAM_MAPPING[full_name.split("."     )[-1]]
			snake_case__				:   int     =						"""param"""

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

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

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







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

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

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


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







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]  ,						snake_case_      :							Optional[int]  ,						snake_case_      :							Tuple     ):
	snake_case__				:   Union[str, Any]     =						[]
	snake_case__				:   Dict     =						fairseq_model.state_dict()

	snake_case__				:   List[Any]     =						hf_model.wavaveca.feature_extractor

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

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







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







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							str  ,						snake_case_      :							List[Any]=None  ,						snake_case_      :							List[Any]=None  ,						snake_case_      :							Any=True  ,						snake_case_      :							Optional[int]=False     ):







	if config_path is not None:
		snake_case__				:   Optional[Any]     =						WavaVecaConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   Any     =						WavaVecaConfig()

	if is_seq_class:
		snake_case__				:   int     =						read_txt_into_dict(snake_case_     )
		snake_case__				:   List[str]     =						idalabel
		snake_case__				:   Union[str, Any]     =						WavaVecaForSequenceClassification(snake_case_     )
		snake_case__				:   Optional[Any]     =						WavaVecaFeatureExtractor(
		    feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=snake_case_  ,						return_attention_mask=snake_case_  ,						)
		feature_extractor.save_pretrained(snake_case_     )

	elif is_finetuned:
		if dict_path:
			snake_case__				:   List[Any]     =						Dictionary.load(snake_case_     )

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

			# fairseq has the <pad> and <s> switched
			snake_case__				:   List[str]     =						0
			snake_case__				:   List[str]     =						1
			with open(snake_case_  ,						"w"  ,						encoding="utf-8"     ) as vocab_handle:
				json.dump(snake_case_  ,						snake_case_     )
			snake_case__				:   Optional[int]     =						WavaVecaCTCTokenizer(
			    snake_case_  ,						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=snake_case_  ,						)
			snake_case__				:   Optional[int]     =						True if config.feat_extract_norm == """layer""" else False
			snake_case__				:   Optional[int]     =						WavaVecaFeatureExtractor(
			    feature_size=1  ,						sampling_rate=16000  ,						padding_value=0  ,						do_normalize=snake_case_  ,						return_attention_mask=snake_case_  ,						)
			snake_case__				:   Optional[int]     =						WavaVecaProcessor(feature_extractor=snake_case_  ,						tokenizer=snake_case_     )
			processor.save_pretrained(snake_case_     )

		snake_case__				:   List[str]     =						WavaVecaForCTC(snake_case_     )
	else:
		snake_case__				:   Optional[Any]     =						WavaVecaForPreTraining(snake_case_     )

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

		snake_case__				:   Any     =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,						task=snake_case_     )

	snake_case__				:   Any     =						model[0].eval()

	recursively_load_weights(snake_case_  ,						snake_case_  ,						not is_finetuned     )

	hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    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"""
	)
	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      :    Dict   		=   parser.parse_args()

	__lowerCamelCase      :    List[str]   		=   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,
	)











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
	import torch

	from transformers.activations import gelu_new, gelu_python, get_activation



@require_torch
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def  _lowercase  (      self  :						Tuple ):
						snake_case__				:   List[str]     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Tuple     =						get_activation("gelu" )
						self.assertTrue(torch.allclose(gelu_python(__A )							,	torch_builtin(__A ) ) )
						self.assertFalse(torch.allclose(gelu_python(__A )							,	gelu_new(__A ) ) )



					def  _lowercase  (      self  :						Dict ):
						snake_case__				:   str     =						torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0] )
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						snake_case__				:   int     =						get_activation("gelu_10" )

						snake_case__				:   Optional[int]     =						torch_builtin(__A )
						snake_case__				:   Dict     =						geluaa(__A )

						snake_case__				:   Optional[Any]     =						torch.where(y_gelu_aa < 1_0.0							,	1							,	0 )

						self.assertTrue(torch.max(__A ).item() == 1_0.0 )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask							,	y_gelu_aa * clipped_mask ) )



					def  _lowercase  (      self  :						str ):
						get_activation("gelu" )
						get_activation("gelu_10" )
						get_activation("gelu_fast" )
						get_activation("gelu_new" )
						get_activation("gelu_python" )
						get_activation("gelu_pytorch_tanh" )
						get_activation("linear" )
						get_activation("mish" )
						get_activation("quick_gelu" )
						get_activation("relu" )
						get_activation("sigmoid" )
						get_activation("silu" )
						get_activation("swish" )
						get_activation("tanh" )
						with self.assertRaises(__A ):
							get_activation("bogus" )
						with self.assertRaises(__A ):
							get_activation(__A )






					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   List[str]     =						get_activation("gelu" )
						snake_case__				:   Any     =						1
						snake_case__				:   Union[str, Any]     =						get_activation("gelu" )
						self.assertEqual(acta.a							,	1 )
						with self.assertRaises(__A ):
							snake_case__				:   int     =						acta.a



