

import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


lowerCAmelCase							:  Dict            =						"""bert-base-cased"""
lowerCAmelCase							:  Dict            =						"""google/pegasus-xsum"""
lowerCAmelCase							:  List[Any]            =						[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCAmelCase							:  int            =						["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCAmelCase							:  Dict            =						"""patrickvonplaten/t5-tiny-random"""
lowerCAmelCase							:  str            =						"""sshleifer/bart-tiny-random"""
lowerCAmelCase							:  Optional[Any]            =						"""sshleifer/tiny-mbart"""
lowerCAmelCase							:  Dict            =						"""sshleifer/tiny-marian-en-de"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"\n".join(_UpperCAmelCase   )
						Path(_UpperCAmelCase   ).open("w"   ).writelines(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase   ):
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(_UpperCAmelCase    ,     f"{split}.source"   )    ,     _UpperCAmelCase   )
												_dump_articles(os.path.join(_UpperCAmelCase    ,     f"{split}.target"   )    ,     _UpperCAmelCase   )
						return tmp_dir



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]       ,						)
			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Tuple  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									SCREAMING_SNAKE_CASE_:			Dict  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in ARTICLES)
									SCREAMING_SNAKE_CASE_:			Dict  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in SUMMARIES)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	4
									SCREAMING_SNAKE_CASE_:			Tuple  		=	8
									assert max_len_target > max_src_len  # Will be truncated
									assert max_len_source > max_src_len  # Will be truncated
									SCREAMING_SNAKE_CASE_:			Tuple  		=	"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
									SCREAMING_SNAKE_CASE_:			Any  		=	SeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=lowerCAmelCase__       ,						max_target_length=lowerCAmelCase__       ,						src_lang=lowerCAmelCase__       ,						tgt_lang=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	DataLoader(lowerCAmelCase__       ,						batch_size=2       ,						collate_fn=train_dataset.collate_fn)
									for batch in dataloader:
															assert isinstance(lowerCAmelCase__       ,						lowerCAmelCase__)
															assert batch["attention_mask"].shape == batch["input_ids"].shape
															# show that articles were trimmed.
															assert batch["input_ids"].shape[1] == max_src_len
															# show that targets are the same len
															assert batch["labels"].shape[1] == max_tgt_len
															if tok_name != MBART_TINY:
																					continue
															# check language codes in correct place
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	shift_tokens_right(batch["labels"]       ,						tokenizer.pad_token_id)
															assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
															assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
															assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
															assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

															break  # No need to test every batch



			@parameterized.expand([BART_TINY, BERT_BASE_CASED])
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Dict):
									SCREAMING_SNAKE_CASE_:			Dict  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in ARTICLES)
									SCREAMING_SNAKE_CASE_:			str  		=	max(len(tokenizer.encode(lowerCAmelCase__)) for a in SUMMARIES)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	4
									SCREAMING_SNAKE_CASE_:			Tuple  		=	LegacySeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=20       ,						max_target_length=lowerCAmelCase__       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	DataLoader(lowerCAmelCase__       ,						batch_size=2       ,						collate_fn=train_dataset.collate_fn)
									for batch in dataloader:
															assert batch["attention_mask"].shape == batch["input_ids"].shape
															# show that articles were trimmed.
															assert batch["input_ids"].shape[1] == max_len_source
															assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
															# show that targets were truncated
															assert batch["labels"].shape[1] == trunc_target  # Truncated
															assert max_len_target > trunc_target  # Truncated
															break  # No need to test every batch



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			str  		=	AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

									SCREAMING_SNAKE_CASE_:			List[str]  		=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	tmp_dir.joinpath("train.source").open().readlines()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
									pack_data_dir(lowerCAmelCase__       ,						lowerCAmelCase__       ,						128       ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{x.name for x in tmp_dir.iterdir()}
									SCREAMING_SNAKE_CASE_:			List[str]  		=	{x.name for x in save_dir.iterdir()}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	save_dir.joinpath("train.source").open().readlines()
									# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
									# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
									assert len(lowerCAmelCase__) < len(lowerCAmelCase__)
									assert len(lowerCAmelCase__) == 1
									assert len(packed_examples[0]) == sum(len(lowerCAmelCase__) for x in orig_examples)
									assert orig_paths == new_paths



			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,						reason="This test requires fairseq")
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
									if not FAIRSEQ_AVAILABLE:
															return
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self._get_dataset(max_len=64)
									SCREAMING_SNAKE_CASE_:			str  		=	64
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	ds.make_dynamic_sampler(lowerCAmelCase__       ,						required_batch_size_multiple=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[len(lowerCAmelCase__) for x in batch_sampler]
									assert len(set(lowerCAmelCase__)) > 1  # it's not dynamic batch size if every batch is the same length
									assert sum(lowerCAmelCase__) == len(lowerCAmelCase__)  # no dropped or added examples
									SCREAMING_SNAKE_CASE_:			int  		=	DataLoader(lowerCAmelCase__       ,						batch_sampler=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2)
									SCREAMING_SNAKE_CASE_:			Dict  		=	[]
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
									for batch in data_loader:
															SCREAMING_SNAKE_CASE_:			Dict  		=	batch["input_ids"].shape
															SCREAMING_SNAKE_CASE_:			str  		=	src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															SCREAMING_SNAKE_CASE_:			Any  		=	np.product(batch["input_ids"].shape)
															num_src_per_batch.append(lowerCAmelCase__)
															if num_src_tokens > (max_tokens * 1.1):
																					failures.append(lowerCAmelCase__)
									assert num_src_per_batch[0] == max(lowerCAmelCase__)
									if failures:
															raise AssertionError(F"too many tokens in {len(lowerCAmelCase__)} batches")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			Dict  		=	self._get_dataset(max_len=512)
									SCREAMING_SNAKE_CASE_:			Dict  		=	2
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	ds.make_sortish_sampler(lowerCAmelCase__       ,						shuffle=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			str  		=	DataLoader(lowerCAmelCase__       ,						batch_size=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2)
									SCREAMING_SNAKE_CASE_:			int  		=	DataLoader(lowerCAmelCase__       ,						batch_size=lowerCAmelCase__       ,						collate_fn=ds.collate_fn       ,						num_workers=2       ,						sampler=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	tokenizer.pad_token_id

									def count_pad_tokens(lowerCAmelCase__  :					Union[str, Any]       ,						lowerCAmelCase__  :					Optional[Any]="input_ids"):
															return [batch[k].eq(lowerCAmelCase__).sum().item() for batch in data_loader]

									assert sum(count_pad_tokens(lowerCAmelCase__       ,						k="labels")) < sum(count_pad_tokens(lowerCAmelCase__       ,						k="labels"))
									assert sum(count_pad_tokens(lowerCAmelCase__)) < sum(count_pad_tokens(lowerCAmelCase__))
									assert len(lowerCAmelCase__) == len(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Any=1000       ,						lowerCAmelCase__  :					Tuple=128):
									if os.getenv("USE_REAL_DATA"       ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	"examples/seq2seq/wmt_en_ro"
															SCREAMING_SNAKE_CASE_:			str  		=	max_len * 2 * 64
															if not Path(lowerCAmelCase__).joinpath("train.len").exists():
																					save_len_file(lowerCAmelCase__       ,						lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	"examples/seq2seq/test_data/wmt_en_ro"
															SCREAMING_SNAKE_CASE_:			Any  		=	max_len * 4
															save_len_file(lowerCAmelCase__       ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	SeqaSeqDataset(
									    lowerCAmelCase__       ,						data_dir=lowerCAmelCase__       ,						type_path="train"       ,						max_source_length=lowerCAmelCase__       ,						max_target_length=lowerCAmelCase__       ,						n_obs=lowerCAmelCase__       ,						)
									return ds, max_tokens, tokenizer



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Any  		=	self._get_dataset()
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	set(DistributedSortishSampler(lowerCAmelCase__       ,						256       ,						num_replicas=2       ,						rank=0       ,						add_extra_examples=lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			int  		=	set(DistributedSortishSampler(lowerCAmelCase__       ,						256       ,						num_replicas=2       ,						rank=1       ,						add_extra_examples=lowerCAmelCase__))
									assert idsa.intersection(lowerCAmelCase__) == set()




			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]       ,						)
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int       ,						lowerCAmelCase__  :					int):
									SCREAMING_SNAKE_CASE_:			Any  		=	AutoTokenizer.from_pretrained(lowerCAmelCase__       ,						use_fast=lowerCAmelCase__)
									if tok_name == MBART_TINY:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	SeqaSeqDataset(
															    lowerCAmelCase__       ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())       ,						type_path="train"       ,						max_source_length=4       ,						max_target_length=8       ,						src_lang="EN"       ,						tgt_lang="FR"       ,						)
															SCREAMING_SNAKE_CASE_:			Any  		=	train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
															SCREAMING_SNAKE_CASE_:			str  		=	SeqaSeqDataset(
															    lowerCAmelCase__       ,						data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())       ,						type_path="train"       ,						max_source_length=4       ,						max_target_length=8       ,						)
															SCREAMING_SNAKE_CASE_:			Dict  		=	train_dataset.dataset_kwargs
															assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
															assert len(lowerCAmelCase__) == 1 if tok_name == BART_TINY else len(lowerCAmelCase__) == 0



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							:  Tuple            =						logging.get_logger(__name__)

lowerCAmelCase							:  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,
}

lowerCAmelCase							:  str            =						{
    """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       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Dict  		=	EfficientNetConfig()
						SCREAMING_SNAKE_CASE_:			Any  		=	CONFIG_MAP[model_name]["hidden_dim"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	CONFIG_MAP[model_name]["width_coef"]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	CONFIG_MAP[model_name]["depth_coef"]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CONFIG_MAP[model_name]["dropout_rate"]
						SCREAMING_SNAKE_CASE_:			Dict  		=	CONFIG_MAP[model_name]["dw_padding"]

						SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
						SCREAMING_SNAKE_CASE_:			str  		=	"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_:			int  		=	10_00
						SCREAMING_SNAKE_CASE_:			int  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}

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







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:			int  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )
						return im







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[v.split("_"   )[0].split("block"   )[1] for v in original_param_names if v.startswith("block"   )]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	sorted(set(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	len(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{b: str(_UpperCAmelCase   ) for b, i in zip(_UpperCAmelCase    ,     range(_UpperCAmelCase   )   )}

						SCREAMING_SNAKE_CASE_:			List[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:
												SCREAMING_SNAKE_CASE_:			List[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")   )

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

						SCREAMING_SNAKE_CASE_:			List[str]  		=	"classifier.weight"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"classifier.bias"
						return key_mapping







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

												SCREAMING_SNAKE_CASE_:			List[str]  		=	key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		SCREAMING_SNAKE_CASE_:			str  		=	torch.from_numpy(_UpperCAmelCase   ).permute(3    ,     2    ,     0    ,     1   )
												elif "depthwise_kernel" in key:
																		SCREAMING_SNAKE_CASE_:			int  		=	torch.from_numpy(_UpperCAmelCase   ).permute(2    ,     3    ,     0    ,     1   )
												elif "kernel" in key:
																		SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.from_numpy(np.transpose(_UpperCAmelCase   )   )
												else:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	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   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	model_classes[model_name](
						    include_top=_UpperCAmelCase    ,     weights="imagenet"    ,     input_tensor=_UpperCAmelCase    ,     input_shape=_UpperCAmelCase    ,     pooling=_UpperCAmelCase    ,     classes=10_00    ,     classifier_activation="softmax"    ,     )

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

						# Load HuggingFace model
						SCREAMING_SNAKE_CASE_:			Any  		=	get_efficientnet_config(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	EfficientNetForImageClassification(_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			str  		=	hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	rename_keys(_UpperCAmelCase   )
						replace_params(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

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

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  List[str]            =						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							:  Any            =						parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






