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


__lowercase										= logging.get_logger(__name__)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						List[Any]      =				["""audio_values""", """audio_mask"""]







    def __init__(     self			,			__lowercase=2_048			,			__lowercase=1			,			__lowercase=[16, 16]			,			__lowercase=128			,			__lowercase=44_100			,			__lowercase=86			,			__lowercase=2_048			,			__lowercase=0.0			,			**__lowercase			,			)	->     List[Any]:
        super().__init__(
            feature_size=__lowercase			,			sampling_rate=__lowercase			,			padding_value=__lowercase			,			**__lowercase			,			)

        __UpperCamelCase					:Optional[Any]        =		spectrogram_length
        __UpperCamelCase					:Tuple        =		num_channels
        __UpperCamelCase					:Optional[int]        =		patch_size
        __UpperCamelCase					:List[Any]        =		feature_size // self.patch_size[1]
        __UpperCamelCase					:List[Any]        =		n_fft
        __UpperCamelCase					:Any        =		sampling_rate // hop_length_to_sampling_rate
        __UpperCamelCase					:Any        =		sampling_rate
        __UpperCamelCase					:Optional[Any]        =		padding_value
        __UpperCamelCase					:Optional[int]        =		mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2			,			num_mel_filters=__lowercase			,			min_frequency=0.0			,			max_frequency=22_050.0			,			sampling_rate=__lowercase			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			).T







    def       UpperCamelCase__       (     self			,			__lowercase)	->     np.ndarray:
        __UpperCamelCase					:int        =		spectrogram(
            __lowercase			,			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=80.0			,			)
        __UpperCamelCase					:Dict        =		log_spec[:, :-1]
        __UpperCamelCase					:List[str]        =		log_spec - 20.0
        __UpperCamelCase					:str        =		np.clip(log_spec / 40.0			,			-2.0			,			0.0) + 1.0
        return log_spec







    def __call__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = True			,			__lowercase = None			,			__lowercase = False			,			__lowercase = False			,			**__lowercase			,			)	->     BatchFeature:

        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.''')

        __UpperCamelCase					:Tuple        =		isinstance(__lowercase			,			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}""")
        __UpperCamelCase					:List[Any]        =		is_batched_numpy or (
            isinstance(__lowercase			,			(list, tuple)) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)))
        )
        if is_batched:
            __UpperCamelCase					:Union[str, Any]        =		[np.asarray([speech]			,			dtype=np.floataa).T for speech in raw_speech]
        elif not is_batched and not isinstance(__lowercase			,			np.ndarray):
            __UpperCamelCase					:int        =		np.asarray(__lowercase			,			dtype=np.floataa)
        elif isinstance(__lowercase			,			np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
            __UpperCamelCase					:str        =		raw_speech.astype(np.floataa)
        # always return batch
        if not is_batched:
            __UpperCamelCase					:Optional[Any]        =		[np.asarray([raw_speech]).T]

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

        # Create audio attention mask
        __UpperCamelCase					:Optional[int]        =		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:
            __UpperCamelCase					:List[str]        =		[
                (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
            ]
            __UpperCamelCase					:Union[str, Any]        =		np.array(__lowercase).astype(np.floataa)

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

        # return as BatchFeature
        if return_attention_mask:
            __UpperCamelCase					:List[Any]        =		{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
        else:
            __UpperCamelCase					:Tuple        =		{'''audio_values''': padded_audio_features}

        __UpperCamelCase					:Union[str, Any]        =		BatchFeature(data=__lowercase			,			tensor_type=__lowercase)
        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


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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''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"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

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

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

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		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] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        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(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= 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.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





