


'''simple docstring'''
import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowerCAmelCase   : int             =				logging.get_logger("""transformers.models.speecht5""")

lowerCAmelCase   : Tuple             =				{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
lowerCAmelCase   : List[str]             =				{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
lowerCAmelCase   : Optional[int]             =				{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
lowerCAmelCase   : str             =				{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
lowerCAmelCase   : Dict             =				{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowerCAmelCase   : Any             =				{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowerCAmelCase   : Union[str, Any]             =				{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
lowerCAmelCase   : Dict             =				{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
lowerCAmelCase   : Optional[Any]             =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase   : str             =				{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase   : List[Any]             =				{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase   : Dict             =				[]
lowerCAmelCase   : Optional[int]             =				[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
lowerCAmelCase   : Tuple             =				IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowerCAmelCase   : List[Any]             =				IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowerCAmelCase   : Any             =				IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				List[Any]:
	for attribute in key.split("."							):
		lowercase						:		List[str]   					=		getattr(_A							,_A							)

	if weight_type is not None:
		lowercase						:		Any   					=		getattr(_A							,_A							).shape
	else:
		lowercase						:		Optional[Any]   					=		hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
		    F''' {value.shape} for {full_name}'''							)

	if weight_type == "weight":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		Optional[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		Union[str, Any]   					=		value
	elif weight_type == "bias":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Any   					=		value
	elif weight_type == "running_var":
		lowercase						:		int   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Dict   					=		value
	else:
		lowercase						:		Optional[Any]   					=		value

	logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''							)




def 	_A  (       A							,A							)  ->				List[Any]:
	for key in ignore_keys:
		if key.endswith(".*"							):
			if name.startswith(key[:-1]							):
				return True
		elif ".*." in key:
			lowercase						:		Dict   					=		key.split(".*."							)
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False




def 	_A  (       A							,A							,A							)  ->				Any:
	lowercase						:		int   					=		[]

	if task == "s2t":
		lowercase						:		Tuple   					=		hf_model.speechta.encoder.prenet.feature_encoder
		lowercase						:		List[Any]   					=		MAPPING_S2T
		lowercase						:		Union[str, Any]   					=		IGNORE_KEYS_S2T
	elif task == "t2s":
		lowercase						:		Optional[int]   					=		None
		lowercase						:		str   					=		MAPPING_T2S
		lowercase						:		Optional[int]   					=		IGNORE_KEYS_T2S
	elif task == "s2s":
		lowercase						:		Optional[Any]   					=		hf_model.speechta.encoder.prenet.feature_encoder
		lowercase						:		Optional[int]   					=		MAPPING_S2S
		lowercase						:		int   					=		IGNORE_KEYS_S2S
	else:
		raise ValueError(F'''Unsupported task: {task}'''							)

	for name, value in fairseq_dict.items():
		if should_ignore(_A							,_A							):
			logger.info(F'''{name} was ignored'''							)
			continue

		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    _A							,_A							,_A							,_A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Optional[int]   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					lowercase						:		List[Any]   					=		key.split(".*."							)
					if prefix in name and suffix in name:
						lowercase						:		int   					=		suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					lowercase						:		List[Any]   					=		True
					if "*" in mapped_key:
						lowercase						:		Dict   					=		name.split(_A							)[0].split("."							)[-2]
						lowercase						:		Tuple   					=		mapped_key.replace("*"							,_A							)
					if "weight_g" in name:
						lowercase						:		Optional[int]   					=		"""weight_g"""
					elif "weight_v" in name:
						lowercase						:		List[Any]   					=		"""weight_v"""
					elif "bias" in name:
						lowercase						:		List[str]   					=		"""bias"""
					elif "weight" in name:
						lowercase						:		Union[str, Any]   					=		"""weight"""
					elif "running_mean" in name:
						lowercase						:		Any   					=		"""running_mean"""
					elif "running_var" in name:
						lowercase						:		Optional[Any]   					=		"""running_var"""
					elif "num_batches_tracked" in name:
						lowercase						:		List[Any]   					=		"""num_batches_tracked"""
					else:
						lowercase						:		Any   					=		None
					set_recursively(_A							,_A							,_A							,_A							,_A							)
				continue
		if not is_used:
			unused_weights.append(_A							)

	logger.warning(F'''Unused weights: {unused_weights}'''							)




def 	_A  (       A							,A							,A							,A							,A							)  ->				int:
	lowercase						:		str   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		List[str]   					=		name.split("."							)
	lowercase						:		List[str]   					=		int(items[0]							)
	lowercase						:		int   					=		int(items[1]							)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
			lowercase						:		Any   					=		value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
			lowercase						:		Dict   					=		value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
			lowercase						:		List[Any]   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
			lowercase						:		int   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
	else:
		unused_weights.append(_A							)




@torch.no_grad()
def 	_A  (       A							,A							,A							,A=None							,A=None							,A=None							,)  ->				int:
	if config_path is not None:
		lowercase						:		str   					=		SpeechTaConfig.from_pretrained(_A							)
	else:
		lowercase						:		List[Any]   					=		SpeechTaConfig()

	if task == "s2t":
		lowercase						:		List[Any]   					=		config.max_text_positions
		lowercase						:		Optional[int]   					=		SpeechTaForSpeechToText(_A							)
	elif task == "t2s":
		lowercase						:		Dict   					=		1_8_7_6
		lowercase						:		str   					=		6_0_0
		lowercase						:		Union[str, Any]   					=		config.max_speech_positions
		lowercase						:		Tuple   					=		SpeechTaForTextToSpeech(_A							)
	elif task == "s2s":
		lowercase						:		Tuple   					=		1_8_7_6
		lowercase						:		Dict   					=		config.max_speech_positions
		lowercase						:		Any   					=		SpeechTaForSpeechToSpeech(_A							)
	else:
		raise ValueError(F'''Unknown task name: {task}'''							)

	if vocab_path:
		lowercase						:		List[str]   					=		SpeechTaTokenizer(_A							,model_max_length=config.max_text_positions							)

		# Mask token behaves like a normal word, i.e. include the space before it
		lowercase						:		int   					=		AddedToken("<mask>"							,lstrip=_A							,rstrip=_A							)
		lowercase						:		Tuple   					=		mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}							)
		tokenizer.add_tokens(["<ctc_blank>"]							)

	lowercase						:		Optional[int]   					=		SpeechTaFeatureExtractor()
	lowercase						:		Optional[int]   					=		SpeechTaProcessor(tokenizer=_A							,feature_extractor=_A							)
	processor.save_pretrained(_A							)

	lowercase						:		List[Any]   					=		torch.load(_A							)
	recursively_load_weights(fairseq_checkpoint["model"]							,_A							,_A							)

	model.save_pretrained(_A							)

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


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				argparse.ArgumentParser()
					parser.add_argument(
					    """--task""",
					    default="""s2t""",
					    type=str,
					    help="""Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.""",
					)
					parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
					parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
					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   : List[str]             =				parser.parse_args()
					convert_speechta_checkpoint(
					    args.task,
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.vocab_path,
					    args.push_to_hub,
					)










"""simple docstring"""







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            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    '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            =	{
    '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      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      List[str]       = EfficientNetConfig()
				lowercase    :      Any       = CONFIG_MAP[model_name]["""hidden_dim"""]
				lowercase    :      List[str]       = CONFIG_MAP[model_name]["""width_coef"""]
				lowercase    :      str       = CONFIG_MAP[model_name]["""depth_coef"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      List[Any]       = CONFIG_MAP[model_name]["""dropout_rate"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""dw_padding"""]

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

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


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Any       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Optional[int]       = Image.open(requests.get(_A  ,				stream=_A ).raw )
				return im


def      UpperCamelCase       ( _A )     ->       Optional[Any]:
				lowercase    :      str       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      Optional[int]       = EfficientNetImageProcessor(
				    size={"""height""": size, """width""": size}  ,				image_mean=[0.485, 0.456, 0.406]  ,				image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]  ,				do_center_crop=_A  ,				)
				return preprocessor


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase    :      Union[str, Any]       = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
				lowercase    :      Optional[Any]       = sorted(set(_A ) )
				lowercase    :      Dict       = len(_A )
				lowercase    :      List[str]       = {b: str(_A ) for b, i in zip(_A  ,				range(_A ) )}

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

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

				lowercase    :      Any       = """classifier.weight"""
				lowercase    :      Tuple       = """classifier.bias"""
				return key_mapping


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				for key, value in tf_params.items():
								if "normalization" in key:
												continue

								lowercase    :      List[Any]       = key_mapping[key]
								if "_conv" in key and "kernel" in key:
												lowercase    :      str       = torch.from_numpy(_A ).permute(3  ,				2  ,				0  ,				1 )
								elif "depthwise_kernel" in key:
												lowercase    :      Optional[int]       = torch.from_numpy(_A ).permute(2  ,				3  ,				0  ,				1 )
								elif "kernel" in key:
												lowercase    :      List[Any]       = torch.from_numpy(np.transpose(_A ) )
								else:
												lowercase    :      Optional[int]       = torch.from_numpy(_A )

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


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Any       = model_classes[model_name](
				    include_top=_A  ,				weights="""imagenet"""  ,				input_tensor=_A  ,				input_shape=_A  ,				pooling=_A  ,				classes=1_000  ,				classifier_activation="""softmax"""  ,				)

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

				# Load HuggingFace model
				lowercase    :      str       = get_efficientnet_config(_A )
				lowercase    :      List[Any]       = EfficientNetForImageClassification(_A ).eval()
				lowercase    :      Optional[int]       = hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("""Converting parameters...""" )
				lowercase    :      int       = rename_keys(_A )
				replace_params(_A  ,				_A  ,				_A )

				# Initialize preprocessor and preprocess input image
				lowercase    :      Optional[int]       = convert_image_processor(_A )
				lowercase    :      Any       = preprocessor(images=prepare_img()  ,				return_tensors="""pt""" )

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								lowercase    :      Union[str, Any]       = hf_model(**_A )
				lowercase    :      List[Any]       = outputs.logits.detach().numpy()

				# Original model inference
				lowercase    :      Optional[Any]       = False
				lowercase    :      str       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      Optional[Any]       = prepare_img().resize((image_size, image_size)  ,				resample=PIL.Image.NEAREST )
				lowercase    :      Optional[Any]       = image.img_to_array(_A )
				lowercase    :      Dict       = np.expand_dims(_A  ,				axis=0 )
				lowercase    :      List[str]       = original_model.predict(_A )

				# Check whether original and HF model outputs match  -> np.allclose
				assert np.allclose(_A  ,				_A  ,				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(_A ):
												os.mkdir(_A )
								# Save converted model and image processor
								hf_model.save_pretrained(_A )
								preprocessor.save_pretrained(_A )

				if push_to_hub:
								# Push model and image processor to hub
								print(F"""Pushing converted {model_name} to the hub...""" )
								lowercase    :      Dict       = F"""efficientnet-{model_name}"""
								preprocessor.push_to_hub(_A )
								hf_model.push_to_hub(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	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            =	parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
