from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Optional[Any]      =				"""roformer"""







    def __init__(     self			,			__lowercase=50_000			,			__lowercase=None			,			__lowercase=768			,			__lowercase=12			,			__lowercase=12			,			__lowercase=3_072			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=1_536			,			__lowercase=2			,			__lowercase=0.02			,			__lowercase=1E-1_2			,			__lowercase=0			,			__lowercase=False			,			__lowercase=True			,			**__lowercase			,			)	->     List[Any]:
        super().__init__(pad_token_id=__lowercase			,			**__lowercase)

        __UpperCamelCase					:str        =		vocab_size
        __UpperCamelCase					:Union[str, Any]        =		hidden_size if embedding_size is None else embedding_size
        __UpperCamelCase					:Optional[Any]        =		hidden_size
        __UpperCamelCase					:Any        =		num_hidden_layers
        __UpperCamelCase					:Optional[Any]        =		num_attention_heads
        __UpperCamelCase					:Optional[int]        =		hidden_act
        __UpperCamelCase					:Union[str, Any]        =		intermediate_size
        __UpperCamelCase					:Union[str, Any]        =		hidden_dropout_prob
        __UpperCamelCase					:Union[str, Any]        =		attention_probs_dropout_prob
        __UpperCamelCase					:Optional[int]        =		max_position_embeddings
        __UpperCamelCase					:List[Any]        =		type_vocab_size
        __UpperCamelCase					:List[str]        =		initializer_range
        __UpperCamelCase					:List[str]        =		layer_norm_eps
        __UpperCamelCase					:Union[str, Any]        =		rotary_value
        __UpperCamelCase					:int        =		use_cache


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @property
    def       UpperCamelCase__       (     self)	->     Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            __UpperCamelCase					:Optional[Any]        =		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
            __UpperCamelCase					:Optional[int]        =		{0: '''batch''', 1: '''sequence'''}
        __UpperCamelCase					:Optional[int]        =		{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
                ('''token_type_ids''', dynamic_axis),
            ])






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





