
"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__      :    Tuple    							= {
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    Any    							= [
					    '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''Wav2Vec2ForAudioFrameClassification''',
					    '''Wav2Vec2ForCTC''',
					    '''Wav2Vec2ForMaskedLM''',
					    '''Wav2Vec2ForPreTraining''',
					    '''Wav2Vec2ForSequenceClassification''',
					    '''Wav2Vec2ForXVector''',
					    '''Wav2Vec2Model''',
					    '''Wav2Vec2PreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    List[str]    							= [
					    '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFWav2Vec2ForCTC''',
					    '''TFWav2Vec2Model''',
					    '''TFWav2Vec2PreTrainedModel''',
					    '''TFWav2Vec2ForSequenceClassification''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__      :    List[Any]    							= [
					    '''FlaxWav2Vec2ForCTC''',
					    '''FlaxWav2Vec2ForPreTraining''',
					    '''FlaxWav2Vec2Model''',
					    '''FlaxWav2Vec2PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
					from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
					from .processing_wavaveca import WavaVecaProcessor
					from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_wavaveca import (
										    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    WavaVecaForAudioFrameClassification,
										    WavaVecaForCTC,
										    WavaVecaForMaskedLM,
										    WavaVecaForPreTraining,
										    WavaVecaForSequenceClassification,
										    WavaVecaForXVector,
										    WavaVecaModel,
										    WavaVecaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_wavaveca import (
										    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFWavaVecaForCTC,
										    TFWavaVecaForSequenceClassification,
										    TFWavaVecaModel,
										    TFWavaVecaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_wavaveca import (
										    FlaxWavaVecaForCTC,
										    FlaxWavaVecaForPreTraining,
										    FlaxWavaVecaModel,
										    FlaxWavaVecaPreTrainedModel,
										)


else:
					import sys

					UpperCAmelCase__      :    List[Any]    							= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case						:Any      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__snake_case						:List[Any]      		=     {
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				EfficientNetConfig()
    __a						       =				CONFIG_MAP[model_name]['''hidden_dim''']
    __a						       =				CONFIG_MAP[model_name]['''width_coef''']
    __a						       =				CONFIG_MAP[model_name]['''depth_coef''']
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				CONFIG_MAP[model_name]['''dropout_rate''']
    __a						       =				CONFIG_MAP[model_name]['''dw_padding''']

    __a						       =				'''huggingface/label-files'''
    __a						       =				'''imagenet-1k-id2label.json'''
    __a						       =				1000
    __a						       =				json.load(open(hf_hub_download(_UpperCAmelCase   , _UpperCAmelCase   , repo_type='''dataset'''				)   , '''r'''				)				)
    __a						       =				{int(_UpperCAmelCase				): v for k, v in idalabel.items()}

    __a						       =				idalabel
    __a						       =				{v: k for k, v in idalabel.items()}
    return config





def      __snake_case	(			):
    __a						       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a						       =				Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase				).raw				)
    return im





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}   , image_mean=[0.4_85, 0.4_56, 0.4_06]   , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]   , do_center_crop=_UpperCAmelCase   , )
    return preprocessor





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
    __a						       =				sorted(set(_UpperCAmelCase				)				)
    __a						       =				len(_UpperCAmelCase				)
    __a						       =				{b: str(_UpperCAmelCase				) for b, i in zip(_UpperCAmelCase   , range(_UpperCAmelCase				)				)}

    __a						       =				[]
    rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''')				)
    rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''')				)
    rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''')				)
    rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''')				)
    rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''')				)

    for b in block_names:
        __a						       =				block_name_mapping[b]
        rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight')				)
        rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight')				)
        rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias')				)
        rename_keys.append(
            (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')				)
        rename_keys.append(
            (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')				)
        rename_keys.append(
            (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')				)
        rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')				)
        rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')				)
        rename_keys.append(
            (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')				)
        rename_keys.append(
            (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')				)

        rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')				)
        rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')				)
        rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')				)
        rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')				)
        rename_keys.append(
            (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight')				)
        rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight')				)
        rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias')				)
        rename_keys.append(
            (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean')				)
        rename_keys.append(
            (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var')				)

    rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')				)
    rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')				)
    rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')				)
    rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')				)
    rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')				)

    __a						       =				{}
    for item in rename_keys:
        if item[0] in original_param_names:
            __a						       =				'''efficientnet.''' + item[1]

    __a						       =				'''classifier.weight'''
    __a						       =				'''classifier.bias'''
    return key_mapping





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        __a						       =				key_mapping[key]
        if "_conv" in key and "kernel" in key:
            __a						       =				torch.from_numpy(_UpperCAmelCase				).permute(3   , 2   , 0   , 1				)
        elif "depthwise_kernel" in key:
            __a						       =				torch.from_numpy(_UpperCAmelCase				).permute(2   , 3   , 0   , 1				)
        elif "kernel" in key:
            __a						       =				torch.from_numpy(np.transpose(_UpperCAmelCase				)				)
        else:
            __a						       =				torch.from_numpy(_UpperCAmelCase				)

        # Replace HF parameters with original TF model parameters
        assert hf_params[hf_key].shape == new_hf_value.shape
        hf_params[hf_key].copy_(_UpperCAmelCase				)





@torch.no_grad()
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				model_classes[model_name](
        include_top=_UpperCAmelCase   , weights='''imagenet'''   , input_tensor=_UpperCAmelCase   , input_shape=_UpperCAmelCase   , pooling=_UpperCAmelCase   , classes=1000   , classifier_activation='''softmax'''   , )

    __a						       =				original_model.trainable_variables
    __a						       =				original_model.non_trainable_variables
    __a						       =				{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        __a						       =				param.numpy()
    __a						       =				list(tf_params.keys()				)

    # Load HuggingFace model
    __a						       =				get_efficientnet_config(_UpperCAmelCase				)
    __a						       =				EfficientNetForImageClassification(_UpperCAmelCase				).eval()
    __a						       =				hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''				)
    __a						       =				rename_keys(_UpperCAmelCase				)
    replace_params(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Initialize preprocessor and preprocess input image
    __a						       =				convert_image_processor(_UpperCAmelCase				)
    __a						       =				preprocessor(images=prepare_img()   , return_tensors='''pt'''				)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        __a						       =				hf_model(**_UpperCAmelCase				)
    __a						       =				outputs.logits.detach().numpy()

    # Original model inference
    __a						       =				False
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				prepare_img().resize((image_size, image_size)   , resample=PIL.Image.NEAREST				)
    __a						       =				image.img_to_array(_UpperCAmelCase				)
    __a						       =				np.expand_dims(_UpperCAmelCase   , axis=0				)
    __a						       =				original_model.predict(_UpperCAmelCase				)

    # Check whether original and HF model outputs match  -> np.allclose
    assert np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , atol=1E-3				), "The predicted logits are not the same."
    print('''Model outputs match!'''				)

    if save_model:
        # Create folder to save model
        if not os.path.isdir(_UpperCAmelCase				):
            os.mkdir(_UpperCAmelCase				)
        # Save converted model and image processor
        hf_model.save_pretrained(_UpperCAmelCase				)
        preprocessor.save_pretrained(_UpperCAmelCase				)

    if push_to_hub:
        # Push model and image processor to hub
        print(f'Pushing converted {model_name} to the hub...'				)
        __a						       =				f'efficientnet-{model_name}'
        preprocessor.push_to_hub(_UpperCAmelCase				)
        hf_model.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:int      		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''b0''',
     type=str,
     help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''hf_model''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

 __snake_case						:Optional[int]      		=     parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





