





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

_SCREAMING_SNAKE_CASE					=    {
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
_SCREAMING_SNAKE_CASE					=    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[Any]		,       lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Dict				):
			for attribute in key.split('''.'''				):
						__lowercase										=							getattr(lowerCamelCase_		,       lowerCamelCase_				)

			if weight_type is not None:
						__lowercase										=							getattr(lowerCamelCase_		,       lowerCamelCase_				).shape
			else:
						__lowercase										=							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":
						__lowercase										=							value
			elif weight_type == "weight_g":
						__lowercase										=							value
			elif weight_type == "weight_v":
						__lowercase										=							value
			elif weight_type == "bias":
						__lowercase										=							value
			else:
						__lowercase										=							value

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Any		,       lowerCamelCase_			:  Dict				):
			__lowercase										=							[]
			__lowercase										=							fairseq_model.state_dict()

			__lowercase										=							hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						__lowercase										=							False
						if "conv_layers" in name:
									load_conv_layer(
									    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       hf_model.config.feat_extract_norm == '''group'''		,       )
									__lowercase										=							True
						else:
									for key, mapped_key in MAPPING.items():
												__lowercase										=							'''unispeech_sat.''' + 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]:
															if "layer_norm_for_extract" in name and (".".join(name.split('''.'''				)[:-1]				) != key):
																		# special case since naming is very similar
																		continue
															__lowercase										=							True
															if "*" in mapped_key:
																		__lowercase										=							name.split(lowerCamelCase_				)[0].split('''.'''				)[-2]
																		__lowercase										=							mapped_key.replace('''*'''		,       lowerCamelCase_				)
															if "weight_g" in name:
																		__lowercase										=							'''weight_g'''
															elif "weight_v" in name:
																		__lowercase										=							'''weight_v'''
															elif "bias" in name:
																		__lowercase										=							'''bias'''
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		__lowercase										=							'''weight'''
															else:
																		__lowercase										=							None
															set_recursively(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
												continue
						if not is_used:
									unused_weights.append(lowerCamelCase_				)

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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Union[str, Any]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							full_name.split('''conv_layers.'''				)[-1]
			__lowercase										=							name.split('''.'''				)
			__lowercase										=							int(items[0]				)
			__lowercase										=							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."				)
									__lowercase										=							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."				)
									__lowercase										=							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[layer_id].layer_norm.bias.data.shape} was found."				)
									__lowercase										=							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[layer_id].layer_norm.weight.data.shape} was found."				)
									__lowercase										=							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    _lowerCAmelCase						(				lowerCamelCase_			:  int		,       lowerCamelCase_			:  int		,       lowerCamelCase_			:  str=None		,       lowerCamelCase_			:  Optional[Any]=None		,       lowerCamelCase_			:  str=True				):
			if config_path is not None:
						__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			else:
						__lowercase										=							UniSpeechSatConfig()

			__lowercase										=							''''''

			if is_finetuned:
						__lowercase										=							UniSpeechSatForCTC(lowerCamelCase_				)
			else:
						__lowercase										=							UniSpeechSatForPreTraining(lowerCamelCase_				)

			__lowercase						,		__lowercase						,		__lowercase										=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path]		,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
			__lowercase										=							model[0].eval()

			recursively_load_weights(lowerCamelCase_		,       lowerCamelCase_				)

			hf_wavavec.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    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'''
	)
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)












'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE					=    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE					=    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

	_SCREAMING_SNAKE_CASE					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure)





