



'''simple docstring'''






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


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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		str:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = """\n""".join(lowerCAmelCase_		)
       Path(lowerCAmelCase_		).open("""w"""		).writelines(lowerCAmelCase_		)




def     snake_case_					(     lowerCAmelCase_		)->		int:





       '''simple docstring'''


       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(lowerCAmelCase_       ,   F'''{split}.source'''		)       ,   lowerCAmelCase_		)
              _dump_articles(os.path.join(lowerCAmelCase_       ,   F'''{split}.target'''		)       ,   lowerCAmelCase_		)
       return tmp_dir



class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]  ,)
 @slow
 def _snake_case (							self  ,a_							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = AutoTokenizer.from_pretrained(a_							)
        _UpperCAmelCase  :						Any			     = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
        _UpperCAmelCase  :						Union[str, Any]			     = max(len(tokenizer.encode(a_							)							) for a in ARTICLES							)
        _UpperCAmelCase  :						Tuple			     = max(len(tokenizer.encode(a_							)							) for a in SUMMARIES							)
        _UpperCAmelCase  :						Any			     = 4
        _UpperCAmelCase  :						Optional[int]			     = 8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        _UpperCAmelCase     ,_UpperCAmelCase  :						str			     = """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
        _UpperCAmelCase  :						Dict			     = SeqaSeqDataset(
            a_  ,data_dir=a_  ,type_path="""train"""  ,max_source_length=a_  ,max_target_length=a_  ,src_lang=a_  ,tgt_lang=a_  ,)
        _UpperCAmelCase  :						Tuple			     = DataLoader(a_  ,batch_size=2  ,collate_fn=train_dataset.collate_fn							)
        for batch in dataloader:
               assert isinstance(a_  ,a_							)
               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
               _UpperCAmelCase  :						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 _snake_case (							self  ,a_							)		->   str:
        _UpperCAmelCase  :						Any			     = AutoTokenizer.from_pretrained(a_							)
        _UpperCAmelCase  :						Optional[Any]			     = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
        _UpperCAmelCase  :						List[str]			     = max(len(tokenizer.encode(a_							)							) for a in ARTICLES							)
        _UpperCAmelCase  :						Tuple			     = max(len(tokenizer.encode(a_							)							) for a in SUMMARIES							)
        _UpperCAmelCase  :						Tuple			     = 4
        _UpperCAmelCase  :						List[Any]			     = LegacySeqaSeqDataset(
            a_  ,data_dir=a_  ,type_path="""train"""  ,max_source_length=20  ,max_target_length=a_  ,)
        _UpperCAmelCase  :						int			     = DataLoader(a_  ,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 _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						str			     = AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

        _UpperCAmelCase  :						List[str]			     = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
        _UpperCAmelCase  :						Dict			     = tmp_dir.joinpath("""train.source"""							).open().readlines()
        _UpperCAmelCase  :						Optional[Any]			     = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
        pack_data_dir(a_  ,a_  ,128  ,a_							)
        _UpperCAmelCase  :						List[str]			     = {x.name for x in tmp_dir.iterdir()}
        _UpperCAmelCase  :						Tuple			     = {x.name for x in save_dir.iterdir()}
        _UpperCAmelCase  :						List[str]			     = 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(a_							) < len(a_							)
        assert len(a_							) == 1
        assert len(packed_examples[0]							) == sum(len(a_							) for x in orig_examples							)
        assert orig_paths == new_paths







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







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = self._get_dataset(max_len=512							)
        _UpperCAmelCase  :						Optional[Any]			     = 2
        _UpperCAmelCase  :						Optional[Any]			     = ds.make_sortish_sampler(a_  ,shuffle=a_							)

        _UpperCAmelCase  :						Optional[int]			     = DataLoader(a_  ,batch_size=a_  ,collate_fn=ds.collate_fn  ,num_workers=2							)
        _UpperCAmelCase  :						str			     = DataLoader(a_  ,batch_size=a_  ,collate_fn=ds.collate_fn  ,num_workers=2  ,sampler=a_							)

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

        def count_pad_tokens(a_  ,a_="input_ids"							):
               return [batch[k].eq(a_							).sum().item() for batch in data_loader]

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







 def _snake_case (							self  ,a_=1_000  ,a_=128							)		->   List[str]:
        if os.getenv("""USE_REAL_DATA"""  ,a_							):
               _UpperCAmelCase  :						List[Any]			     = """examples/seq2seq/wmt_en_ro"""
               _UpperCAmelCase  :						int			     = max_len * 2 * 64
               if not Path(a_							).joinpath("""train.len"""							).exists():
                      save_len_file(a_  ,a_							)
        else:
               _UpperCAmelCase  :						Any			     = """examples/seq2seq/test_data/wmt_en_ro"""
               _UpperCAmelCase  :						Tuple			     = max_len * 4
               save_len_file(a_  ,a_							)

        _UpperCAmelCase  :						List[Any]			     = AutoTokenizer.from_pretrained(a_							)
        _UpperCAmelCase  :						Tuple			     = SeqaSeqDataset(
            a_  ,data_dir=a_  ,type_path="""train"""  ,max_source_length=a_  ,max_target_length=a_  ,n_obs=a_  ,)
        return ds, max_tokens, tokenizer







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = self._get_dataset()
        _UpperCAmelCase  :						Any			     = set(DistributedSortishSampler(a_  ,256  ,num_replicas=2  ,rank=0  ,add_extra_examples=a_							)							)
        _UpperCAmelCase  :						Dict			     = set(DistributedSortishSampler(a_  ,256  ,num_replicas=2  ,rank=1  ,add_extra_examples=a_							)							)
        assert idsa.intersection(a_							) == set()







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]  ,)
 def _snake_case (							self  ,a_							)		->   Any:
        _UpperCAmelCase  :						str			     = AutoTokenizer.from_pretrained(a_  ,use_fast=a_							)
        if tok_name == MBART_TINY:
               _UpperCAmelCase  :						Union[str, Any]			     = SeqaSeqDataset(
                   a_  ,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"""  ,)
               _UpperCAmelCase  :						Dict			     = train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
               _UpperCAmelCase  :						Union[str, Any]			     = SeqaSeqDataset(
                   a_  ,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  ,)
               _UpperCAmelCase  :						Union[str, Any]			     = train_dataset.dataset_kwargs
               assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
               assert len(a_							) == 1 if tok_name == BART_TINY else len(a_							) == 0








'''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()
A_							: List[str]						 =	logging.get_logger(__name__)

A_							: 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,
}

A_							: Dict						 =	{
    """b0""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 2_2_4,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_2_8_0,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 2_4_0,
        """dropout_rate""": 0.2,
        """dw_padding""": [1_6],
    },
    """b2""": {
        """hidden_dim""": 1_4_0_8,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 2_6_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 1_6],
    },
    """b3""": {
        """hidden_dim""": 1_5_3_6,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 3_0_0,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 1_8],
    },
    """b4""": {
        """hidden_dim""": 1_7_9_2,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 3_8_0,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_0_4_8,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 4_5_6,
        """dropout_rate""": 0.4,
        """dw_padding""": [1_3, 2_7],
    },
    """b6""": {
        """hidden_dim""": 2_3_0_4,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 5_2_8,
        """dropout_rate""": 0.5,
        """dw_padding""": [3_1],
    },
    """b7""": {
        """hidden_dim""": 2_5_6_0,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 6_0_0,
        """dropout_rate""": 0.5,
        """dw_padding""": [1_8],
    },
}




def     snake_case_					(     lowerCAmelCase_		)->		int:





       '''simple docstring'''


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

       _UpperCAmelCase  :						List[str]			     = """huggingface/label-files"""
       _UpperCAmelCase  :						Optional[Any]			     = """imagenet-1k-id2label.json"""
       _UpperCAmelCase  :						List[str]			     = 1000
       _UpperCAmelCase  :						Optional[int]			     = json.load(open(hf_hub_download(lowerCAmelCase_       ,   lowerCAmelCase_       ,   repo_type="""dataset"""		)       ,   """r"""		)		)
       _UpperCAmelCase  :						Dict			     = {int(lowerCAmelCase_		): v for k, v in idalabel.items()}

       _UpperCAmelCase  :						List[str]			     = idalabel
       _UpperCAmelCase  :						str			     = {v: k for k, v in idalabel.items()}
       return config




def     snake_case_					(     )->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						List[str]			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						Tuple			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




def     snake_case_					(     lowerCAmelCase_		)->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = CONFIG_MAP[model_name]["""image_size"""]
       _UpperCAmelCase  :						Tuple			     = 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=lowerCAmelCase_       ,   )
       return preprocessor




def     snake_case_					(     lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


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

       _UpperCAmelCase  :						List[str]			     = []
       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:
              _UpperCAmelCase  :						Any			     = 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""")		)

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

       _UpperCAmelCase  :						Optional[Any]			     = """classifier.weight"""
       _UpperCAmelCase  :						Optional[Any]			     = """classifier.bias"""
       return key_mapping




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


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

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

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




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = model_classes[model_name](
           include_top=lowerCAmelCase_       ,   weights="""imagenet"""       ,   input_tensor=lowerCAmelCase_       ,   input_shape=lowerCAmelCase_       ,   pooling=lowerCAmelCase_       ,   classes=1000       ,   classifier_activation="""softmax"""       ,   )

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

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

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

       # Initialize preprocessor and preprocess input image
       _UpperCAmelCase  :						str			     = convert_image_processor(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[str]			     = preprocessor(images=prepare_img()       ,   return_tensors="""pt"""		)

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

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

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

       if push_to_hub:
              # Push model and image processor to hub
              print(F'''Pushing converted {model_name} to the hub...'''		)
              _UpperCAmelCase  :						List[Any]			     = F'''efficientnet-{model_name}'''
              preprocessor.push_to_hub(lowerCAmelCase_		)
              hf_model.push_to_hub(lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Dict						 =	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""")

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



