'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

_lowercase						: 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',
    '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',
}
_lowercase						: Optional[Any]						=						[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 							lowerCamelCase	(      UpperCAmelCase__       :					Tuple      ,      UpperCAmelCase__       :					Union[str, Any]      ,      UpperCAmelCase__       :					Any      ,      UpperCAmelCase__       :					List[str]      ,      UpperCAmelCase__       :					Dict )			->      Union[str, Any]:
							for attribute in key.split(""".""" ):
														lowercase_					:			Any										=       getattr(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE )

							if weight_type is not None:
														lowercase_					:			List[Any]										=       getattr(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE ).shape
							else:
														lowercase_					:			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":
														lowercase_					:			Tuple										=       value
							elif weight_type == "weight_g":
														lowercase_					:			str										=       value
							elif weight_type == "weight_v":
														lowercase_					:			Optional[Any]										=       value
							elif weight_type == "bias":
														lowercase_					:			Union[str, Any]										=       value
							else:
														lowercase_					:			List[Any]										=       value

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




def 							lowerCamelCase	(      UpperCAmelCase__       :					Dict      ,      UpperCAmelCase__       :					Optional[Any] )			->      Union[str, Any]:
							lowercase_					:			int										=       []
							lowercase_					:			List[str]										=       fairseq_model.state_dict()

							lowercase_					:			Tuple										=       hf_model.feature_extractor

							# if encoder has different dim to decoder -> use proj_weight
							lowercase_					:			int										=       None

							for name, value in fairseq_dict.items():
														lowercase_					:			List[str]										=       False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      hf_model.config.feat_extract_norm == """group"""      ,      )
																					lowercase_					:			List[str]										=       True
														elif name.split(""".""" )[0] == "proj":
																					lowercase_					:			Tuple										=       fairseq_model.proj
																					lowercase_					:			str										=       True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
																																			lowercase_					:			List[Any]										=       True
																																			if "*" in mapped_key:
																																										lowercase_					:			str										=       name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
																																										lowercase_					:			int										=       mapped_key.replace("""*"""      ,      _SCREAMING_SNAKE_CASE )
																																			if "weight_g" in name:
																																										lowercase_					:			List[Any]										=       'weight_g'
																																			elif "weight_v" in name:
																																										lowercase_					:			List[Any]										=       'weight_v'
																																			elif "bias" in name:
																																										lowercase_					:			Optional[Any]										=       'bias'
																																			elif "weight" in name:
																																										lowercase_					:			Tuple										=       'weight'
																																			else:
																																										lowercase_					:			Optional[Any]										=       None
																																			set_recursively(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE )
																												continue
														if not is_used:
																					unused_weights.append(_SCREAMING_SNAKE_CASE )

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

							return proj_weight




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




def 							lowerCamelCase	(      UpperCAmelCase__       :					int )			->      Union[str, Any]:
							lowercase_					:			List[str]										=       emb.weight.shape
							lowercase_					:			str										=       nn.Linear(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      bias=_SCREAMING_SNAKE_CASE )
							lowercase_					:			Optional[int]										=       emb.weight.data
							return lin_layer




def 							lowerCamelCase	(      UpperCAmelCase__       :					Any )			->      Tuple:
							with open(_SCREAMING_SNAKE_CASE      ,      """r"""      ,      encoding="""utf-8""" ) as f:
														lowercase_					:			Union[str, Any]										=       f.readlines()
														lowercase_					:			Tuple										=       [line.split(""" """ )[0] for line in lines]

							lowercase_					:			int										=       len(_SCREAMING_SNAKE_CASE )

							lowercase_					:			List[Any]										=       {
							    '<s>': 0,
							    '<pad>': 1,
							    '</s>': 2,
							    '<unk>': 3,
							}

							vocab_dict.update(dict(zip(_SCREAMING_SNAKE_CASE      ,      range(4      ,      num_words + 4 ) ) ) )
							return vocab_dict




@torch.no_grad()
def 							lowerCamelCase	(      UpperCAmelCase__       :					List[Any]      ,      UpperCAmelCase__       :					List[Any]      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Any      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Any      ,      UpperCAmelCase__       :					Optional[int]      ,      )			->      str:
							lowercase_					:			Optional[int]										=       WavaVecaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
							lowercase_					:			Any										=       SpeechaTextaConfig.from_pretrained(
							    _SCREAMING_SNAKE_CASE      ,      vocab_size=_SCREAMING_SNAKE_CASE      ,      decoder_layers=_SCREAMING_SNAKE_CASE      ,      do_stable_layer_norm=_SCREAMING_SNAKE_CASE )

							lowercase_					:			Dict										=       WavaVecaFeatureExtractor(
							    feature_size=1      ,      sampling_rate=16000      ,      padding_value=0      ,      do_normalize=_SCREAMING_SNAKE_CASE      ,      return_attention_mask=_SCREAMING_SNAKE_CASE      ,      )

							lowercase_					:			List[Any]										=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]      ,      arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
							lowercase_					:			Optional[int]										=       model[0].eval()

							# set weights for wav2vec2 encoder
							lowercase_					:			Tuple										=       WavaVecaModel(_SCREAMING_SNAKE_CASE )
							lowercase_					:			int										=       recursively_load_weights_wavaveca(model.encoder      ,      _SCREAMING_SNAKE_CASE )

							lowercase_					:			Dict										=       SpeechaTextaForCausalLM(_SCREAMING_SNAKE_CASE )
							lowercase_					:			Optional[int]										=       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,      strict=_SCREAMING_SNAKE_CASE )

							# set output linear layer
							unexpected_keys.remove("""embed_out""" )
							lowercase_					:			Optional[Any]										=       nn.Parameter(model.decoder.embed_out.detach() )

							# layer norm is init to identity matrix so leaving it is fine
							logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
							logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )

							lowercase_					:			Tuple										=       SpeechEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE      ,      decoder=_SCREAMING_SNAKE_CASE )
							lowercase_					:			int										=       False

							# add projection layer
							lowercase_					:			str										=       nn.Parameter(projection_layer.weight )
							lowercase_					:			Any										=       nn.Parameter(projection_layer.bias )

							lowercase_					:			str										=       create_vocab_dict(_SCREAMING_SNAKE_CASE )

							with open(os.path.join(_SCREAMING_SNAKE_CASE      ,      """vocab.json""" )      ,      """w""" ) as fp:
														json.dump(_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE )

							lowercase_					:			Optional[Any]										=       SpeechaTextaTokenizer(os.path.join(_SCREAMING_SNAKE_CASE      ,      """vocab.json""" ) )
							tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )

							lowercase_					:			Union[str, Any]										=       hf_wavavec.config.to_dict()
							lowercase_					:			Tuple										=       tokenizer.pad_token_id
							lowercase_					:			Optional[int]										=       tokenizer.bos_token_id
							lowercase_					:			Union[str, Any]										=       tokenizer.eos_token_id
							lowercase_					:			Tuple										=       'speech_to_text_2'
							lowercase_					:			Tuple										=       'wav2vec2'

							lowercase_					:			List[str]										=       SpeechEncoderDecoderConfig.from_dict(_SCREAMING_SNAKE_CASE )

							hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
							feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
						_lowercase						: Dict						=						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(
						    "--encoder_config_path",
						    default="facebook/wav2vec2-large-lv60",
						    type=str,
						    help="Path to hf encoder wav2vec2 checkpoint config",
						)
						parser.add_argument(
						    "--decoder_config_path",
						    default="facebook/s2t-small-mustc-en-fr-st",
						    type=str,
						    help="Path to hf decoder s2t checkpoint config",
						)
						parser.add_argument("--vocab_size", default=10224, type=int, help="Vocab size of decoder")
						parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

						_lowercase						: Tuple						=						parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    vocab_size=args.vocab_size,
						    num_decoder_layers=args.num_decoder_layers,
						)









'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



