


from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


_snake_case					:		List[Any]      =    logging.get_logger(__name__)


_snake_case					:		Optional[int]      =    OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

_snake_case					:		List[str]      =    OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

_snake_case					:		Dict      =    OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

_snake_case					:		int      =    OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

_snake_case					:		Union[str, Any]      =    OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

_snake_case					:		Union[str, Any]      =    OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

_snake_case					:		Any      =    OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

_snake_case					:		Optional[int]      =    OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

_snake_case					:		Dict      =    OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

_snake_case					:		Union[str, Any]      =    OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

_snake_case					:		int      =    OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

_snake_case					:		List[str]      =    OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

_snake_case					:		str      =    OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

_snake_case					:		Any      =    OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

_snake_case					:		int      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_snake_case					:		List[Any]      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_snake_case					:		int      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_snake_case					:		str      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_snake_case					:		List[Any]      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_snake_case					:		str      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_snake_case					:		int      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_snake_case					:		List[str]      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_snake_case					:		int      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_snake_case					:		int      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_snake_case					:		List[Any]      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_snake_case					:		Dict      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_snake_case					:		Any      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_snake_case					:		Union[str, Any]      =    _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  FLAX_MODEL_MAPPING
_snake_case					:		Optional[Any]      =    auto_class_update(FlaxAutoModel)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  FLAX_MODEL_FOR_PRETRAINING_MAPPING
_snake_case					:		List[Any]      =    auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
_snake_case					:		Optional[Any]      =    auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  FLAX_MODEL_FOR_MASKED_LM_MAPPING
_snake_case					:		Union[str, Any]      =    auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_snake_case					:		str      =    auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
_snake_case					:		Any      =    auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
_snake_case					:		Optional[int]      =    auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_snake_case					:		str      =    auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
_snake_case					:		List[str]      =    auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
_snake_case					:		List[Any]      =    auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
_snake_case					:		Optional[Any]      =    auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
_snake_case					:		Optional[Any]      =    auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")

class     a		(_BaseAutoModelClass				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
_snake_case					:		List[Any]      =    auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)





import math
from typing import Optional

import numpy as np

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


_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "encodec"

							def __init__(							self	:  Any   ,     lowerCamelCase	:  Optional[int]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,     lowerCamelCase	:  List[str]=24000   ,     lowerCamelCase	:  int=1   ,     lowerCamelCase	:  Optional[int]=False   ,     lowerCamelCase	:  Dict=None   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  Optional[int]=128   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  List[str]=1   ,     lowerCamelCase	:  str=[8, 5, 4, 2]   ,     lowerCamelCase	:  List[str]="weight_norm"   ,     lowerCamelCase	:  Any=7   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  List[Any]="reflect"   ,     lowerCamelCase	:  Union[str, Any]=2   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  int=1.0   ,     lowerCamelCase	:  Optional[Any]=1024   ,     lowerCamelCase	:  Optional[Any]=None   ,     lowerCamelCase	:  str=True   ,     **lowerCamelCase	:  Dict   ,     )     ->   Any:
														__snake_case		:      Tuple										=       target_bandwidths
														__snake_case		:      Union[str, Any]										=       sampling_rate
														__snake_case		:      Union[str, Any]										=       audio_channels
														__snake_case		:      Dict										=       normalize
														__snake_case		:      List[Any]										=       chunk_length_s
														__snake_case		:      Tuple										=       overlap
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      List[Any]										=       num_filters
														__snake_case		:      Union[str, Any]										=       num_residual_layers
														__snake_case		:      Optional[int]										=       upsampling_ratios
														__snake_case		:      List[str]										=       norm_type
														__snake_case		:      Optional[int]										=       kernel_size
														__snake_case		:      Dict										=       last_kernel_size
														__snake_case		:      Tuple										=       residual_kernel_size
														__snake_case		:      List[Any]										=       dilation_growth_rate
														__snake_case		:      Optional[int]										=       use_causal_conv
														__snake_case		:      Tuple										=       pad_mode
														__snake_case		:      Union[str, Any]										=       compress
														__snake_case		:      Union[str, Any]										=       num_lstm_layers
														__snake_case		:      int										=       trim_right_ratio
														__snake_case		:      Tuple										=       codebook_size
														__snake_case		:      Optional[Any]										=       codebook_dim if codebook_dim is not None else hidden_size
														__snake_case		:      int										=       use_conv_shortcut

														if self.norm_type not in ["weight_norm", "time_group_norm"]:
																					raise ValueError(
																					    F'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}'    )

														super().__init__(**lowerCamelCase    )

							@property
							def    __snake_case (							self	:  int    )     ->   Optional[int]:
														if self.chunk_length_s is None:
																					return None
														else:
																					return int(self.chunk_length_s * self.sampling_rate    )

							@property
							def    __snake_case (							self	:  Union[str, Any]    )     ->   Optional[int]:
														if self.chunk_length_s is None or self.overlap is None:
																					return None
														else:
																					return max(1   ,     int((1.0 - self.overlap) * self.chunk_length    )    )

							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      Union[str, Any]										=       np.prod(self.upsampling_ratios    )
														return math.ceil(self.sampling_rate / hop_length    )






							@property
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )

