





import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
							import numpy as np
							import torch

							from . import (
							    AlbertForPreTraining,
							    BartForConditionalGeneration,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    CamembertForMaskedLM,
							    CTRLLMHeadModel,
							    DistilBertForMaskedLM,
							    DistilBertForQuestionAnswering,
							    DPRContextEncoder,
							    DPRQuestionEncoder,
							    DPRReader,
							    ElectraForPreTraining,
							    FlaubertWithLMHeadModel,
							    GPTaLMHeadModel,
							    LayoutLMForMaskedLM,
							    LxmertForPreTraining,
							    LxmertVisualFeatureEncoder,
							    OpenAIGPTLMHeadModel,
							    RobertaForMaskedLM,
							    RobertaForSequenceClassification,
							    TaForConditionalGeneration,
							    TransfoXLLMHeadModel,
							    XLMRobertaForMaskedLM,
							    XLMWithLMHeadModel,
							    XLNetLMHeadModel,
							)


logging.set_verbosity_info()

__lowerCAmelCase :Any     =    {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True				):
					if model_type not in MODEL_CLASSES:
										raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}."""				)

					_snake_case       , _snake_case       , _snake_case       , _snake_case       :					str				       =   MODEL_CLASSES[model_type]

					# Initialise TF model
					if config_file in aws_config_map:
										_snake_case       :					List[str]				       =   cached_file(UpperCAmelCase  ,  UpperCAmelCase  ,  force_download=not use_cached_models				)
					_snake_case       :					List[str]				       =   config_class.from_json_file(UpperCAmelCase				)
					_snake_case       :					str				       =   True
					_snake_case       :					List[Any]				       =   True
					print(F"""Building TensorFlow model from configuration: {config}"""				)
					_snake_case       :					str				       =   model_class(UpperCAmelCase				)

					# Load weights from tf checkpoint
					if pytorch_checkpoint_path in aws_config_map.keys():
										_snake_case       :					Optional[Any]				       =   cached_file(
										    UpperCAmelCase  ,  UpperCAmelCase  ,  force_download=not use_cached_models				)
					# Load PyTorch checkpoint in tf2 model:
					_snake_case       :					List[str]				       =   load_pytorch_checkpoint_in_tfa_model(UpperCAmelCase  ,  UpperCAmelCase				)

					if compare_with_pt_model:
										_snake_case       :					Any				       =   tf_model(tf_model.dummy_inputs  ,  training=UpperCAmelCase				)  # build the network

										_snake_case       :					List[str]				       =   torch.load(UpperCAmelCase  ,  map_location="cpu"				)
										_snake_case       :					int				       =   pt_model_class.from_pretrained(
										    pretrained_model_name_or_path=UpperCAmelCase  ,  config=UpperCAmelCase  ,  state_dict=UpperCAmelCase				)

										with torch.no_grad():
															_snake_case       :					Dict				       =   pt_model(**pt_model.dummy_inputs				)

										_snake_case       :					Union[str, Any]				       =   pto[0].numpy()
										_snake_case       :					Union[str, Any]				       =   tfo[0].numpy()
										_snake_case       :					int				       =   np.amax(np.abs(np_pt - np_tf				)				)
										print(F"""Max absolute difference between models outputs {diff}"""				)
										assert diff <= 2e-2, F"""Error, model absolute difference is >2e-2: {diff}"""

					# Save pytorch-model
					print(F"""Save TensorFlow model to {tf_dump_path}"""				)
					tf_model.save_weights(UpperCAmelCase  ,  save_format="h5"				)







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=None  ,  UpperCAmelCase=None  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  ):
					if args_model_type is None:
										_snake_case       :					Optional[int]				       =   list(MODEL_CLASSES.keys()				)
					else:
										_snake_case       :					Tuple				       =   [args_model_type]

					for j, model_type in enumerate(UpperCAmelCase  ,  start=1				):
										print("=" * 100				)
										print(F""" Converting model type {j}/{len(UpperCAmelCase				)}: {model_type}"""				)
										print("=" * 100				)
										if model_type not in MODEL_CLASSES:
															raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()				)}."""				)

										_snake_case       , _snake_case       , _snake_case       , _snake_case       , _snake_case       :					str				       =   MODEL_CLASSES[model_type]

										if model_shortcut_names_or_path is None:
															_snake_case       :					Dict				       =   list(aws_model_maps.keys()				)
										if config_shortcut_names_or_path is None:
															_snake_case       :					str				       =   model_shortcut_names_or_path

										for i, (model_shortcut_name, config_shortcut_name) in enumerate(
										    zip(UpperCAmelCase  ,  UpperCAmelCase				)  ,  start=1				):
															print("-" * 100				)
															if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																				if not only_convert_finetuned_models:
																									print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""				)
																									continue
																				_snake_case       :					List[str]				       =   model_shortcut_name
															elif only_convert_finetuned_models:
																				print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""				)
																				continue
															print(
															    F"""    Converting checkpoint {i}/{len(UpperCAmelCase				)}: {model_shortcut_name} - model_type {model_type}"""				)
															print("-" * 100				)

															if config_shortcut_name in aws_config_map:
																				_snake_case       :					Optional[int]				       =   cached_file(UpperCAmelCase  ,  UpperCAmelCase  ,  force_download=not use_cached_models				)
															else:
																				_snake_case       :					Union[str, Any]				       =   config_shortcut_name

															if model_shortcut_name in aws_model_maps:
																				_snake_case       :					List[Any]				       =   cached_file(UpperCAmelCase  ,  UpperCAmelCase  ,  force_download=not use_cached_models				)
															else:
																				_snake_case       :					Union[str, Any]				       =   model_shortcut_name

															if os.path.isfile(UpperCAmelCase				):
																				_snake_case       :					Optional[int]				       =   "converted_model"

															convert_pt_checkpoint_to_tf(
															    model_type=UpperCAmelCase  ,  pytorch_checkpoint_path=UpperCAmelCase  ,  config_file=UpperCAmelCase  ,  tf_dump_path=os.path.join(UpperCAmelCase  ,  model_shortcut_name + "-tf_model.h5"				)  ,  compare_with_pt_model=UpperCAmelCase  ,  )
															if remove_cached_files:
																				os.remove(UpperCAmelCase				)
																				os.remove(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :List[str]     =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
							)
							parser.add_argument(
							    '--model_type',
							    default=None,
							    type=str,
							    help=(
							        F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
							        'convert all the models from AWS.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_checkpoint_path',
							    default=None,
							    type=str,
							    help=(
							        'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
							        'If not given, will download and convert all the checkpoints from AWS.'
							    ),
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    help=(
							        'The config json file corresponding to the pre-trained model. \n'
							        'This specifies the model architecture. If not given and '
							        '--pytorch_checkpoint_path is not given or is a shortcut name '
							        'use the configuration associated to the shortcut name on the AWS'
							    ),
							)
							parser.add_argument(
							    '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
							)
							parser.add_argument(
							    '--use_cached_models',
							    action='store_true',
							    help='Use cached models if possible instead of updating to latest checkpoint versions.',
							)
							parser.add_argument(
							    '--remove_cached_files',
							    action='store_true',
							    help='Remove pytorch models after conversion (save memory when converting in batches).',
							)
							parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
							__lowerCAmelCase :List[Any]     =    parser.parse_args()

							# if args.pytorch_checkpoint_path is not None:
							#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
							#                                 args.pytorch_checkpoint_path,
							#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
							#                                 args.tf_dump_path,
							#                                 compare_with_pt_model=args.compare_with_pt_model,
							#                                 use_cached_models=args.use_cached_models)
							# else:
							convert_all_pt_checkpoints_to_tf(
							    args.model_type.lower() if args.model_type is not None else None,
							    args.tf_dump_path,
							    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
							    if args.pytorch_checkpoint_path is not None
							    else None,
							    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
							    compare_with_pt_model=args.compare_with_pt_model,
							    use_cached_models=args.use_cached_models,
							    remove_cached_files=args.remove_cached_files,
							    only_convert_finetuned_models=args.only_convert_finetuned_models,
							)






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()
__lowerCAmelCase :str     =    logging.get_logger(__name__)

__lowerCAmelCase :int     =    {
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

__lowerCAmelCase :List[str]     =    {
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}







def    A				(							UpperCAmelCase				):
					_snake_case       :					str				       =   EfficientNetConfig()
					_snake_case       :					Optional[int]				       =   CONFIG_MAP[model_name]["hidden_dim"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["width_coef"]
					_snake_case       :					Dict				       =   CONFIG_MAP[model_name]["depth_coef"]
					_snake_case       :					List[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					Tuple				       =   CONFIG_MAP[model_name]["dropout_rate"]
					_snake_case       :					str				       =   CONFIG_MAP[model_name]["dw_padding"]

					_snake_case       :					Union[str, Any]				       =   "huggingface/label-files"
					_snake_case       :					Any				       =   "imagenet-1k-id2label.json"
					_snake_case       :					Optional[Any]				       =   1_000
					_snake_case       :					Optional[Any]				       =   json.load(open(hf_hub_download(UpperCAmelCase  ,  UpperCAmelCase  ,  repo_type="dataset"				)  ,  "r"				)				)
					_snake_case       :					int				       =   {int(UpperCAmelCase				): v for k, v in idalabel.items()}

					_snake_case       :					Optional[int]				       =   idalabel
					_snake_case       :					List[Any]				       =   {v: k for k, v in idalabel.items()}
					return config







def    A				(							):
					_snake_case       :					List[Any]				       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
					_snake_case       :					Optional[int]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				)
					return im







def    A				(							UpperCAmelCase				):
					_snake_case       :					Optional[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					int				       =   EfficientNetImageProcessor(
					    size={"height": size, "width": size}  ,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,  image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]  ,  do_center_crop=UpperCAmelCase  ,  )
					return preprocessor







def    A				(							UpperCAmelCase				):
					_snake_case       :					Tuple				       =   [v.split("_"				)[0].split("block"				)[1] for v in original_param_names if v.startswith("block"				)]
					_snake_case       :					str				       =   sorted(set(UpperCAmelCase				)				)
					_snake_case       :					Any				       =   len(UpperCAmelCase				)
					_snake_case       :					str				       =   {b: str(UpperCAmelCase				) for b, i in zip(UpperCAmelCase  ,  range(UpperCAmelCase				)				)}

					_snake_case       :					Optional[int]				       =   []
					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:
										_snake_case       :					Optional[int]				       =   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")				)

					_snake_case       :					Union[str, Any]				       =   {}
					for item in rename_keys:
										if item[0] in original_param_names:
															_snake_case       :					str				       =   "efficientnet." + item[1]

					_snake_case       :					List[str]				       =   "classifier.weight"
					_snake_case       :					int				       =   "classifier.bias"
					return key_mapping







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

										_snake_case       :					Union[str, Any]				       =   key_mapping[key]
										if "_conv" in key and "kernel" in key:
															_snake_case       :					Union[str, Any]				       =   torch.from_numpy(UpperCAmelCase				).permute(3  ,  2  ,  0  ,  1				)
										elif "depthwise_kernel" in key:
															_snake_case       :					str				       =   torch.from_numpy(UpperCAmelCase				).permute(2  ,  3  ,  0  ,  1				)
										elif "kernel" in key:
															_snake_case       :					Optional[Any]				       =   torch.from_numpy(np.transpose(UpperCAmelCase				)				)
										else:
															_snake_case       :					int				       =   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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					Optional[int]				       =   model_classes[model_name](
					    include_top=UpperCAmelCase  ,  weights="imagenet"  ,  input_tensor=UpperCAmelCase  ,  input_shape=UpperCAmelCase  ,  pooling=UpperCAmelCase  ,  classes=1_000  ,  classifier_activation="softmax"  ,  )

					_snake_case       :					int				       =   original_model.trainable_variables
					_snake_case       :					str				       =   original_model.non_trainable_variables
					_snake_case       :					List[Any]				       =   {param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										_snake_case       :					str				       =   param.numpy()
					_snake_case       :					int				       =   list(tf_params.keys()				)

					# Load HuggingFace model
					_snake_case       :					Optional[int]				       =   get_efficientnet_config(UpperCAmelCase				)
					_snake_case       :					Dict				       =   EfficientNetForImageClassification(UpperCAmelCase				).eval()
					_snake_case       :					int				       =   hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("Converting parameters..."				)
					_snake_case       :					Optional[int]				       =   rename_keys(UpperCAmelCase				)
					replace_params(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase				)

					# Initialize preprocessor and preprocess input image
					_snake_case       :					Optional[Any]				       =   convert_image_processor(UpperCAmelCase				)
					_snake_case       :					List[str]				       =   preprocessor(images=prepare_img()  ,  return_tensors="pt"				)

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										_snake_case       :					Tuple				       =   hf_model(**UpperCAmelCase				)
					_snake_case       :					str				       =   outputs.logits.detach().numpy()

					# Original model inference
					_snake_case       :					List[Any]				       =   False
					_snake_case       :					Optional[Any]				       =   CONFIG_MAP[model_name]["image_size"]
					_snake_case       :					Optional[int]				       =   prepare_img().resize((image_size, image_size)  ,  resample=PIL.Image.NEAREST				)
					_snake_case       :					List[Any]				       =   image.img_to_array(UpperCAmelCase				)
					_snake_case       :					Optional[int]				       =   np.expand_dims(UpperCAmelCase  ,  axis=0				)
					_snake_case       :					int				       =   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..."""				)
										_snake_case       :					Optional[Any]				       =   F"""efficientnet-{model_name}"""
										preprocessor.push_to_hub(UpperCAmelCase				)
										hf_model.push_to_hub(UpperCAmelCase				)


if __name__ == "__main__":
							__lowerCAmelCase :Any     =    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')

							__lowerCAmelCase :List[Any]     =    parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)