import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
    from .features import FeatureType


__lowercase			,	__lowercase			,	__lowercase										= False, False, False


@dataclass
class      lowerCamelCase_					:






    '''simple docstring'''






    a__     :						Optional[int]      =				None
    a__     :						bool      =				True
    a__     :						bool      =				True
    a__     :						Optional[str]      =				None
    # Automatically constructed
    a__     :						ClassVar[str]      =				"dict"
    a__     :						ClassVar[Any]      =				pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}			)
    a__     :						str      =				field(default="""Audio"""						,     init=UpperCAmelCase_						,     repr=UpperCAmelCase_			)







    def __call__(     self)	->     List[Any]:
        return self.pa_type







    def       UpperCamelCase__       (     self			,			__lowercase)	->     dict:
        try:
            import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
        except ImportError as err:
            raise ImportError('''To support encoding audio data, please install \'soundfile\'.''') from err
        if isinstance(__lowercase			,			__lowercase):
            return {"bytes": None, "path": value}
        elif isinstance(__lowercase			,			__lowercase):
            return {"bytes": value, "path": None}
        elif "array" in value:
            # convert the audio array to wav bytes
            __UpperCamelCase					:Any        =		BytesIO()
            sf.write(__lowercase			,			value['''array''']			,			value['''sampling_rate''']			,			format='''wav''')
            return {"bytes": buffer.getvalue(), "path": None}
        elif value.get('''path''') is not None and os.path.isfile(value['''path''']):
            # we set "bytes": None to not duplicate the data if they're already available locally
            if value["path"].endswith('''pcm'''):
                # "PCM" only has raw audio bytes
                if value.get('''sampling_rate''') is None:
                    # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
                    raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object''')
                if value.get('''bytes'''):
                    # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
                    __UpperCamelCase					:Optional[int]        =		np.frombuffer(value['''bytes''']			,			dtype=np.intaa).astype(np.floataa) / 32_767
                else:
                    __UpperCamelCase					:List[str]        =		np.memmap(value['''path''']			,			dtype='''h'''			,			mode='''r''').astype(np.floataa) / 32_767

                __UpperCamelCase					:Dict        =		BytesIO(bytes())
                sf.write(__lowercase			,			__lowercase			,			value['''sampling_rate''']			,			format='''wav''')
                return {"bytes": buffer.getvalue(), "path": None}
            else:
                return {"bytes": None, "path": value.get('''path''')}
        elif value.get('''bytes''') is not None or value.get('''path''') is not None:
            # store the audio bytes, and path is used to infer the audio format using the file extension
            return {"bytes": value.get('''bytes'''), "path": value.get('''path''')}
        else:
            raise ValueError(
                f"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""")







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     dict:
        if not self.decode:
            raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.''')

        __UpperCamelCase  ,	__UpperCamelCase					:Optional[int]        =		(value['''path'''], BytesIO(value['''bytes'''])) if value['''bytes'''] is not None else (value['''path'''], None)
        if path is None and file is None:
            raise ValueError(f"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}.""")

        try:
            import librosa
            import soundfile as sf
        except ImportError as err:
            raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.''') from err

        __UpperCamelCase					:Optional[int]        =		xsplitext(__lowercase)[1][1:].lower() if path is not None else None
        if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
            raise RuntimeError(
                '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
                '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''')
        elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
            raise RuntimeError(
                '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
                '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. ''')

        if file is None:
            __UpperCamelCase					:List[Any]        =		token_per_repo_id or {}
            __UpperCamelCase					:Any        =		path.split('''::''')[-1]
            try:
                __UpperCamelCase					:List[str]        =		string_to_dict(__lowercase			,			config.HUB_DATASETS_URL)['''repo_id''']
                __UpperCamelCase					:Tuple        =		token_per_repo_id[repo_id]
            except (ValueError, KeyError):
                __UpperCamelCase					:Optional[Any]        =		None

            with xopen(__lowercase			,			'''rb'''			,			use_auth_token=__lowercase) as f:
                __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		sf.read(__lowercase)

        else:
            __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		sf.read(__lowercase)

        __UpperCamelCase					:str        =		array.T
        if self.mono:
            __UpperCamelCase					:Tuple        =		librosa.to_mono(__lowercase)
        if self.sampling_rate and self.sampling_rate != sampling_rate:
            __UpperCamelCase					:Any        =		librosa.resample(__lowercase			,			orig_sr=__lowercase			,			target_sr=self.sampling_rate)
            __UpperCamelCase					:List[Any]        =		self.sampling_rate

        return {"path": path, "array": array, "sampling_rate": sampling_rate}







    def       UpperCamelCase__       (     self)	->     Union["FeatureType", Dict[str, "FeatureType"]]:
        from .features import Value

        if self.decode:
            raise ValueError('''Cannot flatten a decoded Audio feature.''')
        return {
            "bytes": Value('''binary'''),
            "path": Value('''string'''),
        }







    def       UpperCamelCase__       (     self			,			__lowercase)	->     pa.StructArray:
        if pa.types.is_string(storage.type):
            __UpperCamelCase					:Dict        =		pa.array([None] * len(__lowercase)			,			type=pa.binary())
            __UpperCamelCase					:str        =		pa.StructArray.from_arrays([bytes_array, storage]			,			['''bytes''', '''path''']			,			mask=storage.is_null())
        elif pa.types.is_binary(storage.type):
            __UpperCamelCase					:Optional[int]        =		pa.array([None] * len(__lowercase)			,			type=pa.string())
            __UpperCamelCase					:Dict        =		pa.StructArray.from_arrays([storage, path_array]			,			['''bytes''', '''path''']			,			mask=storage.is_null())
        elif pa.types.is_struct(storage.type) and storage.type.get_all_field_indices('''array'''):
            __UpperCamelCase					:Dict        =		pa.array([Audio().encode_example(__lowercase) if x is not None else None for x in storage.to_pylist()])
        elif pa.types.is_struct(storage.type):
            if storage.type.get_field_index('''bytes''') >= 0:
                __UpperCamelCase					:Any        =		storage.field('''bytes''')
            else:
                __UpperCamelCase					:str        =		pa.array([None] * len(__lowercase)			,			type=pa.binary())
            if storage.type.get_field_index('''path''') >= 0:
                __UpperCamelCase					:int        =		storage.field('''path''')
            else:
                __UpperCamelCase					:Dict        =		pa.array([None] * len(__lowercase)			,			type=pa.string())
            __UpperCamelCase					:str        =		pa.StructArray.from_arrays([bytes_array, path_array]			,			['''bytes''', '''path''']			,			mask=storage.is_null())
        return array_cast(__lowercase			,			self.pa_type)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     pa.StructArray:

        @no_op_if_value_is_null
        def path_to_bytes(__lowercase):
            with xopen(__lowercase			,			'''rb''') as f:
                __UpperCamelCase					:List[Any]        =		f.read()
            return bytes_

        __UpperCamelCase					:int        =		pa.array(
            [
                (path_to_bytes(x['''path''']) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                for x in storage.to_pylist()
            ]			,			type=pa.binary()			,			)
        __UpperCamelCase					:Optional[int]        =		pa.array(
            [os.path.basename(__lowercase) if path is not None else None for path in storage.field('''path''').to_pylist()]			,			type=pa.string()			,			)
        __UpperCamelCase					:Tuple        =		pa.StructArray.from_arrays([bytes_array, path_array]			,			['''bytes''', '''path''']			,			mask=bytes_array.is_null())
        return array_cast(__lowercase			,			self.pa_type)






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)





