






"""simple docstring"""
import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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",
    "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",
}
_lowerCAmelCase       =				[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	for attribute in key.split("""."""       ):
		A_		:    str						     = getattr(snake_case__ , snake_case__       )

	if weight_type is not None:
		A_		:    List[Any]						     = getattr(snake_case__ , snake_case__       ).shape
	else:
		A_		:    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_		:    Tuple						     = value
	elif weight_type == "bias":
		A_		:    int						     = value
	else:
		A_		:    Union[str, Any]						     = value

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






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    List[Any]						     = []
	A_		:    Any						     = fairseq_model.state_dict()

	A_		:    List[Any]						     = hf_model.feature_extractor
	A_		:    Optional[Any]						     = hf_model.adapter

	for name, value in fairseq_dict.items():
		A_		:    Union[str, Any]						     = False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case__ , snake_case__ , snake_case__ , snake_case__ , hf_model.config.feat_extract_norm == """group""" , )
			A_		:    int						     = True
		elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]       ):
			load_adapter(snake_case__ , snake_case__ , snake_case__ , snake_case__       )
			A_		:    str						     = True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
					A_		:    List[Any]						     = True
					if "*" in mapped_key:
						A_		:    Dict						     = name.split(snake_case__       )[0].split("""."""       )[-2]
						A_		:    List[Any]						     = mapped_key.replace("""*""" , snake_case__       )
					if "weight_g" in name:
						A_		:    str						     = """weight_g"""
					elif "weight_v" in name:
						A_		:    Union[str, Any]						     = """weight_v"""
					elif "bias" in name:
						A_		:    str						     = """bias"""
					elif "weight" in name:
						A_		:    Optional[Any]						     = """weight"""
					else:
						A_		:    Tuple						     = None
					set_recursively(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )
				continue
		if not is_used:
			unused_weights.append(snake_case__       )

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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Dict						     = full_name.split("""conv_layers."""       )[-1]
	A_		:    Any						     = name.split("""."""       )
	A_		:    Optional[int]						     = int(items[0]       )
	A_		:    Tuple						     = 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_		:    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."""
			)
			A_		:    Dict						     = 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_		:    Optional[int]						     = 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_		:    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__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Optional[Any]						     = full_name.split("""adaptor."""       )[-1]
	A_		:    Dict						     = name.split("""."""       )

	if items[1].isdigit():
		A_		:    Optional[int]						     = int(items[1]       )
	else:
		A_		:    Optional[Any]						     = None

	if "adaptor" not in full_name:
		if "proj_ln" in full_name:
			# has to be layer norm
			if "bias" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.bias.data.shape
				), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
				A_		:    List[Any]						     = value
				logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""       )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj_layer_norm.weight.data.shape
				), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
				A_		:    Optional[Any]						     = value
		else:
			# has to be projection layer
			if "bias" in name:
				assert (
				    value.shape == adapter.proj.bias.data.shape
				), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
				A_		:    Optional[int]						     = value
				logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""       )
			if "weight" in name:
				assert (
				    value.shape == adapter.proj.weight.data.shape
				), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
				A_		:    Optional[int]						     = value
				logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""       )
	elif isinstance(snake_case__ , snake_case__       ):
		if "bias" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.bias.data.shape
			), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
			A_		:    Optional[Any]						     = value
			logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
		elif "weight" in name:
			assert (
			    value.shape == adapter.layers[layer_id].conv.weight.data.shape
			), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
			A_		:    Tuple						     = value
			logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
	else:
		unused_weights.append(snake_case__       )






def       __UpperCamelCase						(		snake_case__       ):
	A_     ,  A_		:    Optional[Any]						     = emb.weight.shape
	A_		:    int						     = nn.Linear(snake_case__ , snake_case__ , bias=snake_case__       )
	A_		:    Optional[int]						     = emb.weight.data
	return lin_layer






@torch.no_grad()
def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
	A_		:    Any						     = WavaVecaConfig.from_pretrained(
	    snake_case__ , add_adapter=snake_case__ , adapter_stride=snake_case__ , adapter_kernel_size=snake_case__ , use_auth_token=snake_case__ , output_hidden_size=snake_case__ , )
	A_		:    Optional[Any]						     = MBartConfig.from_pretrained(snake_case__       )

	# load model
	A_     ,  A_     ,  A_		:    Optional[Any]						     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path] , arg_overrides={
	        """config_yaml""": config_yaml_path,
	        """data""": """/""".join(dict_path.split("""/"""       )[:-1]       ),
	        """w2v_path""": checkpoint_path,
	        """load_pretrained_decoder_from""": None,
	    } , )
	A_		:    Optional[Any]						     = model[0].eval()

	# load feature extractor
	A_		:    List[Any]						     = WavaVecaFeatureExtractor.from_pretrained(snake_case__ , use_auth_token=snake_case__       )

	# set weights for wav2vec2 encoder
	A_		:    List[str]						     = WavaVecaModel(snake_case__       )

	recursively_load_weights_wavaveca(model.encoder , snake_case__       )

	# load decoder weights
	A_		:    List[Any]						     = MBartForCausalLM(snake_case__       )
	A_     ,  A_		:    List[str]						     = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=snake_case__       )
	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}"""       )

	A_		:    Optional[Any]						     = SpeechEncoderDecoderModel(encoder=snake_case__ , decoder=snake_case__       )
	A_		:    Any						     = False

	A_		:    Union[str, Any]						     = MBartaaTokenizer(snake_case__       )
	tokenizer.save_pretrained(snake_case__       )

	A_		:    Optional[Any]						     = hf_wavavec.config.to_dict()
	A_		:    Optional[Any]						     = tokenizer.pad_token_id
	A_		:    int						     = tokenizer.bos_token_id
	A_		:    List[Any]						     = tokenizer.eos_token_id
	A_		:    str						     = """mbart50"""
	A_		:    Any						     = """wav2vec2"""

	A_		:    List[str]						     = tokenizer.eos_token_id
	A_		:    Dict						     = 250_004
	A_		:    Tuple						     = tokenizer.eos_token_id

	A_		:    List[str]						     = SpeechEncoderDecoderConfig.from_dict(snake_case__       )

	hf_wavavec.save_pretrained(snake_case__       )
	feature_extractor.save_pretrained(snake_case__       )


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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
							parser.add_argument(
							    "--encoder_config_path",
							    default="facebook/wav2vec2-xls-r-1b",
							    type=str,
							    help="Path to hf encoder wav2vec2 checkpoint config",
							)
							parser.add_argument(
							    "--decoder_config_path",
							    default="facebook/mbart-large-50-one-to-many-mmt",
							    type=str,
							    help="Path to hf decoder checkpoint config",
							)
							parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
							parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
							parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
							parser.add_argument("--encoder_output_dim", default=1_024, type=int, help="encoder output dim")
							parser.add_argument("--start_token_id", default=250_004, type=int, help="`decoder_start_token_id` of model config")

							_lowerCAmelCase       =				parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    args.config_yaml_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    add_adapter=args.add_adapter,
							    adapter_kernel_size=args.adapter_kernel_size,
							    adapter_stride=args.adapter_stride,
							    decoder_start_token_id=args.start_token_id,
							    encoder_output_dim=args.encoder_output_dim,
							)









"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase       =				logging.get_logger(__name__)

_lowerCAmelCase       =				{
    "huggingface/informer-tourism-monthly": (
        "https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[Any] 			=					"""informer"""
	_A							:					Dict 			=					{
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}





	def __init__(self  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "student_t"  ,  lowerCAmelCase_ = "nll"  ,  lowerCAmelCase_ = 1  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "mean"  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = 0  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = 64  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 32  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = 2  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "gelu"  ,  lowerCAmelCase_ = 0.05  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 0.1  ,  lowerCAmelCase_ = 100  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_=True  ,  lowerCAmelCase_ = "prob"  ,  lowerCAmelCase_ = 5  ,  lowerCAmelCase_ = True  ,  **lowerCAmelCase_  ,  ):
		# time series specific configuration
		A_		:    Optional[Any]						     = prediction_length
		A_		:    Dict						     = context_length or prediction_length
		A_		:    Dict						     = distribution_output
		A_		:    Tuple						     = loss
		A_		:    Dict						     = input_size
		A_		:    Union[str, Any]						     = num_time_features
		A_		:    List[str]						     = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
		A_		:    Optional[int]						     = scaling
		A_		:    Optional[Any]						     = num_dynamic_real_features
		A_		:    Tuple						     = num_static_real_features
		A_		:    Tuple						     = num_static_categorical_features

		# set cardinality
		if cardinality and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    List[str]						     = cardinality
		else:
			A_		:    List[Any]						     = [0]

		# set embedding_dimension
		if embedding_dimension and num_static_categorical_features > 0:
			if len(lowerCAmelCase_				) != num_static_categorical_features:
				raise ValueError(
				    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
			A_		:    int						     = embedding_dimension
		else:
			A_		:    Optional[int]						     = [min(50  ,  (cat + 1) // 2				) for cat in self.cardinality]

		A_		:    Optional[int]						     = num_parallel_samples

		# Transformer architecture configuration
		A_		:    Union[str, Any]						     = input_size * len(self.lags_sequence				) + self._number_of_features
		A_		:    Dict						     = d_model
		A_		:    Dict						     = encoder_attention_heads
		A_		:    Dict						     = decoder_attention_heads
		A_		:    Any						     = encoder_ffn_dim
		A_		:    Tuple						     = decoder_ffn_dim
		A_		:    Tuple						     = encoder_layers
		A_		:    Optional[int]						     = decoder_layers

		A_		:    List[str]						     = dropout
		A_		:    List[str]						     = attention_dropout
		A_		:    Any						     = activation_dropout
		A_		:    Any						     = encoder_layerdrop
		A_		:    List[Any]						     = decoder_layerdrop

		A_		:    str						     = activation_function
		A_		:    Optional[Any]						     = init_std

		A_		:    Optional[int]						     = use_cache

		# Informer
		A_		:    Dict						     = attention_type
		A_		:    List[Any]						     = sampling_factor
		A_		:    List[Any]						     = distil

		super().__init__(is_encoder_decoder=lowerCAmelCase_  ,  **lowerCAmelCase_				)





	@property
	def  lowerCamelCase(self				):
		return (
		    sum(self.embedding_dimension				)
		    + self.num_dynamic_real_features
		    + self.num_time_features
		    + self.num_static_real_features
		    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
		)

