
from collections import OrderedDict

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


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


lowerCamelCase_						: str         =  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"""),
    ]
)

lowerCamelCase_						: Any         =  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"""),
    ]
)

lowerCamelCase_						: Union[str, Any]         =  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"""),
    ]
)

lowerCamelCase_						: List[str]         =  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"""),
    ]
)

lowerCamelCase_						: Tuple         =  OrderedDict(
    [
        # Model for Image-classsification
        ("""beit""", """FlaxBeitForImageClassification"""),
        ("""regnet""", """FlaxRegNetForImageClassification"""),
        ("""resnet""", """FlaxResNetForImageClassification"""),
        ("""vit""", """FlaxViTForImageClassification"""),
    ]
)

lowerCamelCase_						: Optional[int]         =  OrderedDict(
    [
        ("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
    ]
)

lowerCamelCase_						: Optional[int]         =  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"""),
    ]
)

lowerCamelCase_						: Dict         =  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"""),
    ]
)

lowerCamelCase_						: Any         =  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"""),
    ]
)

lowerCamelCase_						: List[str]         =  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"""),
    ]
)

lowerCamelCase_						: 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"""),
    ]
)

lowerCamelCase_						: Union[str, Any]         =  OrderedDict(
    [
        ("""bert""", """FlaxBertForNextSentencePrediction"""),
    ]
)

lowerCamelCase_						: Optional[int]         =  OrderedDict(
    [
        ("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
        ("""whisper""", """FlaxWhisperForConditionalGeneration"""),
    ]
)

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

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

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Tuple					      = FLAX_MODEL_MAPPING




lowerCamelCase_						: str         =  auto_class_update(FlaxAutoModel)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = FLAX_MODEL_FOR_PRETRAINING_MAPPING




lowerCamelCase_						: List[str]         =  auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Any					      = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




lowerCamelCase_						: Dict         =  auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = FLAX_MODEL_FOR_MASKED_LM_MAPPING




lowerCamelCase_						: Dict         =  auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




lowerCamelCase_						: int         =  auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Tuple					      = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




lowerCamelCase_						: Dict         =  auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




lowerCamelCase_						: Any         =  auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Dict					      = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




lowerCamelCase_						: List[str]         =  auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




lowerCamelCase_						: Tuple         =  auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




lowerCamelCase_						: Optional[int]         =  auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




lowerCamelCase_						: Dict         =  auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="""image classification"""
)

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						Union[str, Any]					      = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




lowerCamelCase_						: Optional[int]         =  auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")

class        _UpperCamelCase				(					_BaseAutoModelClass  ):






			'''simple docstring'''

			__UpperCamelCase      :						int					      = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




lowerCamelCase_						: Any         =  auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.bias''']

				for i in range(len(config.upsample_rates						) * len(config.resblock_kernel_sizes						)						):
								for j in range(len(config.resblock_dilation_sizes						)						):
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

												UpperCamelCase_:							Union[str, Any]	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
												UpperCamelCase_:							int	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

				UpperCamelCase_:							str	=						torch.load(lowerCamelCase						)
				load_weights(orig_checkpoint["""model"""]["""generator"""]     ,    lowerCamelCase     ,    lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[Any]	=						torch.from_numpy(lowerCamelCase						).float()

				model.save_pretrained(lowerCamelCase						)

				if repo_id:
								print("""Pushing to the hub..."""						)
								model.push_to_hub(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
							parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							lowerCamelCase_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






