





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					__a					):

						'''simple docstring'''





						__magic_name__:			Union[str, Any]       =       ["audio_values", "audio_mask"]






						def __init__(      self      :      Dict		,   _A      :      Tuple=2048		,   _A      :      str=1		,   _A      :      Any=[16, 16]		,   _A      :      str=128		,   _A      :      int=44100		,   _A      :      Optional[Any]=86		,   _A      :      int=2048		,   _A      :      Optional[Any]=0.0		,   **_A      :      Union[str, Any]		,   )							->    Optional[int]:







							"""simple docstring"""


							super().__init__(
							    feature_size=a_		,   sampling_rate=a_		,   padding_value=a_		,   **a_		,   )

							snake_case_		:			Optional[int]											=	spectrogram_length
							snake_case_		:			Dict											=	num_channels
							snake_case_		:			int											=	patch_size
							snake_case_		:			Any											=	feature_size // self.patch_size[1]
							snake_case_		:			List[Any]											=	n_fft
							snake_case_		:			List[str]											=	sampling_rate // hop_length_to_sampling_rate
							snake_case_		:			int											=	sampling_rate
							snake_case_		:			Dict											=	padding_value
							snake_case_		:			Optional[int]											=	mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		,   num_mel_filters=a_		,   min_frequency=0.0		,   max_frequency=22050.0		,   sampling_rate=a_		,   norm='slaney'		,   mel_scale='slaney'		,   ).T






						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      np.array	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	spectrogram(
							    a_		,   window_function(self.n_fft		,   'hann'	)		,   frame_length=self.n_fft		,   hop_length=self.hop_length		,   power=2.0		,   mel_filters=self.mel_filters.T		,   log_mel='dB'		,   db_range=8_0.0		,   )
							snake_case_		:			Dict											=	log_spec[:, :-1]
							snake_case_		:			Union[str, Any]											=	log_spec - 2_0.0
							snake_case_		:			int											=	np.clip(log_spec / 4_0.0		,   -2.0		,   0.0	) + 1.0
							return log_spec






						def __call__(      self      :      Any		,   _A      :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,   _A      :      Optional[Union[str, TensorType]] = None		,   _A      :      Optional[bool] = True		,   _A      :      Optional[int] = None		,   _A      :      bool = False		,   _A      :      bool = False		,   **_A      :      str		,   )							->    Dict:







							"""simple docstring"""


							if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
									raise ValueError(
									    'This feature extractor is set to support sampling rate'
									    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
									    F""" with {self.sampling_rate} and not {sampling_rate}."""	)
							else:
								logger.warning(
								    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
								    'Failing to do so can result in silent errors that might be hard to debug.'	)

							snake_case_		:			str											=	isinstance(a_		,   np.ndarray	) and len(raw_speech.shape	) > 1
							if is_batched_numpy and len(raw_speech.shape	) > 2:
								raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""	)
							snake_case_		:			Optional[Any]											=	is_batched_numpy or (
							    isinstance(a_		,   (list, tuple)	) and (isinstance(raw_speech[0]		,   (np.ndarray, tuple, list)	))
							)
							if is_batched:
								snake_case_		:			List[str]											=	[np.asarray([speech]		,   dtype=np.floataa	).T for speech in raw_speech]
							elif not is_batched and not isinstance(a_		,   np.ndarray	):
								snake_case_		:			int											=	np.asarray(a_		,   dtype=np.floataa	)
							elif isinstance(a_		,   np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								snake_case_		:			str											=	raw_speech.astype(np.floataa	)
							# always return batch
							if not is_batched:
								snake_case_		:			List[str]											=	[np.asarray([raw_speech]	).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							snake_case_		:			Dict											=	[
							    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]		,   a_	):
								snake_case_		:			str											=	[np.asarray(a_		,   dtype=np.floataa	) for feature in audio_features]

							# Create audio attention mask
							snake_case_		:			Any											=	max(
							    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
							if return_attention_mask:
								snake_case_		:			Any											=	[
								    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
								    for feature in audio_features
								]
								snake_case_		:			Optional[int]											=	np.array(a_	).astype(np.floataa	)

							# convert into correct format for padding
							snake_case_		:			Optional[int]											=	max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							snake_case_		:			Optional[Any]											=	np.ones([len(a_	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
							snake_case_		:			Any											=	padded_audio_features * self.padding_value
							for i in range(len(a_	)	):
								snake_case_		:			Optional[Any]											=	audio_features[i]
								snake_case_		:			Optional[int]											=	feature

							# return as BatchFeature
							if return_attention_mask:
								snake_case_		:			Dict											=	{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
								snake_case_		:			Optional[Any]											=	{"""audio_values""": padded_audio_features}

							snake_case_		:			int											=	BatchFeature(data=a_		,   tensor_type=a_	)
							return encoded_inputs








import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):
	if "xprophetnet" in prophetnet_checkpoint_path:
		snake_case_		:			int											=	XLMProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			List[Any]											=	XLMProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )
	else:
		snake_case_		:			List[str]											=	ProphetNetForConditionalGenerationOld.from_pretrained(__a   )
		snake_case_						,snake_case_		:			Dict											=	ProphetNetForConditionalGeneration.from_pretrained(
		    __a   , output_loading_info=__a   )

	snake_case_		:			str											=	['key_proj', 'value_proj', 'query_proj']

	snake_case_		:			List[str]											=	{
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		snake_case_		:			Union[str, Any]											=	key.split('.'   )

		if attributes[0] == "lm_head":
			snake_case_		:			Optional[Any]											=	prophet
			snake_case_		:			Any											=	prophet_old
		else:
			snake_case_		:			Optional[int]											=	prophet.prophetnet
			snake_case_		:			str											=	prophet_old.model

		snake_case_		:			Union[str, Any]											=	False
		for attribute in attributes:
			if attribute in mapping:
				snake_case_		:			Optional[Any]											=	mapping[attribute]
				if not hasattr(__a   , __a   ) and len(__a   ) > 0:
					snake_case_		:			List[Any]											=	attribute
			elif hasattr(__a   , __a   ):
				snake_case_		:			List[Any]											=	attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				snake_case_		:			int											=	old_model.weight
				logger.info(f"""{attribute} is initialized."""   )
				snake_case_		:			Optional[int]											=	True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				snake_case_		:			List[str]											=	old_model.bias
				logger.info(f"""{attribute} is initialized"""   )
				snake_case_		:			int											=	True
				break
			elif attribute in special_keys and hasattr(__a   , 'in_proj_weight'   ):
				snake_case_		:			Optional[Any]											=	old_model.in_proj_weight.shape[0] // 3
				snake_case_		:			List[Any]											=	getattr(__a   , __a   )
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					snake_case_		:			Tuple											=	nn.Parameter(old_model.in_proj_weight[:embed_dim, :]   )
					snake_case_		:			Optional[int]											=	nn.Parameter(old_model.in_proj_bias[:embed_dim]   )

				elif attribute == "key_proj":
					snake_case_		:			int											=	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]   )
					snake_case_		:			Any											=	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]   )
				elif attribute == "value_proj":
					snake_case_		:			List[Any]											=	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]   )
					snake_case_		:			str											=	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]   )
				snake_case_		:			Any											=	True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
				snake_case_		:			Any											=	nn.Parameter(old_model.embed_positions.weight[:5_12, :]   )
				snake_case_		:			List[Any]											=	True
				break

			if attribute.isdigit():
				snake_case_		:			Any											=	model[int(__a   )]
				snake_case_		:			Any											=	old_model[int(__a   )]
			else:
				snake_case_		:			str											=	getattr(__a   , __a   )

				if old_attribute == "":
					snake_case_		:			Tuple											=	old_model
				else:
					if not hasattr(__a   , __a   ):
						raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
					snake_case_		:			List[str]											=	getattr(__a   , __a   )

		if not is_key_init:
			raise ValueError(f"""{key} was not correctly initialized!"""   )

	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	prophet.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

