





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Any:  # picklable for multiprocessing
  return x.sum()




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:  # picklable for multiprocessing
  return i + 1
@dataclass
class        SCREAMING_SNAKE_CASE     :
   _UpperCamelCase					:	int
   _UpperCamelCase					:	str






class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						{}
     lowercase__			    =						[]
     lowercase__			    =						1
     lowercase__			    =						[1, 2]
     lowercase__			    =						{'a': 1, 'b': 2}
     lowercase__			    =						{'a': [1, 2], 'b': [3, 4]}
     lowercase__			    =						{'a': {'1': 1}, 'b': 2}
     lowercase__			    =						{'a': 1, 'b': 2, 'c': 3, 'd': 4}
     lowercase__			    =						{}
     lowercase__			    =						[]
     lowercase__			    =						2
     lowercase__			    =						[2, 3]
     lowercase__			    =						{'a': 2, 'b': 3}
     lowercase__			    =						{'a': [2, 3], 'b': [4, 5]}
     lowercase__			    =						{'a': {'1': 2}, 'b': 3}
     lowercase__			    =						{'a': 2, 'b': 3, 'c': 4, 'd': 5}
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )
     self.assertEqual(map_nested(a    ,  a )    ,  a )

     lowercase__			    =						2
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )
     self.assertEqual(map_nested(a    ,  a    ,  num_proc=a )    ,  a )

     lowercase__			    =						{'a': np.eye(2 ), 'b': np.zeros(3 ), 'c': np.ones(2 )}
     lowercase__			    =						{'a': 2, 'b': 0, 'c': 2}
     lowercase__			    =						{
         'a': np.eye(2 ).astype(a ),
         'b': np.zeros(3 ).astype(a ),
         'c': np.ones(2 ).astype(a ),
     }
     self.assertEqual(map_nested(a    ,  a    ,  map_numpy=a )    ,  a )
     self.assertEqual(
         {k: v.tolist() for k, v in map_nested(a    ,  a    ,  map_numpy=a ).items()}    ,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}    ,  )
     self.assertEqual(map_nested(a    ,  a    ,  map_numpy=a    ,  num_proc=a )    ,  a )
     self.assertEqual(
         {k: v.tolist() for k, v in map_nested(a    ,  a    ,  map_numpy=a    ,  num_proc=a ).items()}    ,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}    ,  )
     with self.assertRaises(a ):  # can't pickle a local lambda
       map_nested(lambda a : x + 1    ,  a    ,  num_proc=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any] )->     Any:





     """simple docstring"""







     lowercase__			    =						{'a': 1, 'b': 2}
     lowercase__			    =						{'a': 3, 'b': 4}
     lowercase__			    =						{'a': 5, 'b': 6}
     lowercase__			    =						sorted([('a', (1, 3, 5)), ('b', (2, 4, 6))] )
     self.assertEqual(sorted(zip_dict(a    ,  a    ,  a ) )    ,  a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     int:





     """simple docstring"""







     class        SCREAMING_SNAKE_CASE     :
        _UpperCamelCase					:	Any				      = 'bar'






     lowercase__			    =						Foo()
     self.assertEqual(foo.my_attr    ,  'bar' )
     with temporary_assignment(a    ,  'my_attr'    ,  'BAR' ):
       self.assertEqual(foo.my_attr    ,  'BAR' )
     self.assertEqual(foo.my_attr    ,  'bar' )






@pytest.mark.parametrize(
    'iterable_length, num_proc, expected_num_proc'	,		[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]	,		)
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Any:
  with patch('datasets.utils.py_utils._single_map_nested'	) as mock_single_map_nested, patch(
      'datasets.parallel.parallel.Pool'	) as mock_multiprocessing_pool:
    lowercase__			    =						{F"""{i}""": i for i in range(_SCREAMING_SNAKE_CASE	)}
    lowercase__			    =						map_nested(lambda _SCREAMING_SNAKE_CASE	: x + 10	,		_SCREAMING_SNAKE_CASE	,		num_proc=_SCREAMING_SNAKE_CASE	,		parallel_min_length=16	)
    if expected_num_proc == 1:
      assert mock_single_map_nested.called
      assert not mock_multiprocessing_pool.called
    else:
      assert not mock_single_map_nested.called
      assert mock_multiprocessing_pool.called
      assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):


   @require_tf
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     str:





     """simple docstring"""







     import tensorflow as tf
     from tensorflow.keras import layers

     lowercase__			    =						layers.Dense(2 )

     def gen_random_output():
       lowercase__			    =						tf.random.uniform((1, 3) )
       return model(a ).numpy()

     with temp_seed(42    ,  set_tensorflow=a ):
       lowercase__			    =						gen_random_output()
     with temp_seed(42    ,  set_tensorflow=a ):
       lowercase__			    =						gen_random_output()
     lowercase__			    =						gen_random_output()

     np.testing.assert_equal(a    ,  a )
     self.assertGreater(np.abs(outa - outa ).sum()    ,  0 )


   @require_torch
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Any:





     """simple docstring"""







     import torch

     def gen_random_output():
       lowercase__			    =						torch.nn.Linear(3    ,  2 )
       lowercase__			    =						torch.rand(1    ,  3 )
       return model(a ).detach().numpy()

     with temp_seed(42    ,  set_pytorch=a ):
       lowercase__			    =						gen_random_output()
     with temp_seed(42    ,  set_pytorch=a ):
       lowercase__			    =						gen_random_output()
     lowercase__			    =						gen_random_output()

     np.testing.assert_equal(a    ,  a )
     self.assertGreater(np.abs(outa - outa ).sum()    ,  0 )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Tuple:





     """simple docstring"""







     def gen_random_output():
       return np.random.rand(1    ,  3 )

     with temp_seed(42 ):
       lowercase__			    =						gen_random_output()
     with temp_seed(42 ):
       lowercase__			    =						gen_random_output()
     lowercase__			    =						gen_random_output()

     np.testing.assert_equal(a    ,  a )
     self.assertGreater(np.abs(outa - outa ).sum()    ,  0 )






@pytest.mark.parametrize('input_data'	,		[{}]	)
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Any:
  lowercase__			    =						NestedDataStructure(_SCREAMING_SNAKE_CASE	).data
  assert output_data == input_data




@pytest.mark.parametrize(
    'data, expected_output'	,		[
        ({}, []),
        ([], []),
        ('foo', ['foo']),
        (['foo', 'bar'], ['foo', 'bar']),
        ([['foo', 'bar']], ['foo', 'bar']),
        ([[['foo'], ['bar']]], ['foo', 'bar']),
        ([[['foo'], 'bar']], ['foo', 'bar']),
        ({'a': 1, 'b': 2}, [1, 2]),
        ({'a': [1, 2], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[1, 2]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [[3, 4]]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, 4]}, [1, 2, 3, 4]),
        ({'a': [[[1], [2]]], 'b': [3, [4]]}, [1, 2, 3, 4]),
        ({'a': {'1': 1}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': 2}, [1, 2]),
        ({'a': {'1': [1]}, 'b': [2]}, [1, 2]),
    ]	,		)
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  lowercase__			    =						NestedDataStructure(_SCREAMING_SNAKE_CASE	).flatten()
  assert output == expected_output




def     __UpperCamelCase						()       ->     int:
  lowercase__			    =						A(x=1	,		y='foobar'	)
  lowercase__			    =						{'x': 1, 'y': 'foobar'}
  assert asdict(_SCREAMING_SNAKE_CASE	) == expected_output

  lowercase__			    =						{'a': {'b': A(x=10	,		y='foo'	)}, 'c': [A(x=20	,		y='bar'	)]}
  lowercase__			    =						{'a': {'b': {'x': 10, 'y': 'foo'}}, 'c': [{'x': 20, 'y': 'bar'}]}
  assert asdict(_SCREAMING_SNAKE_CASE	) == expected_output

  with pytest.raises(_SCREAMING_SNAKE_CASE	):
    asdict([1, A(x=10	,		y='foo'	)]	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Any:
  return text.split()




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  yield (time.time(), content)
  time.sleep(2	)
  yield (time.time(), content)




def     __UpperCamelCase						()       ->     Any:
  with Pool(2	) as pool:
    lowercase__			    =						list(iflatmap_unordered(_SCREAMING_SNAKE_CASE	,		_split_text	,		kwargs_iterable=[{'text': 'hello there'}] * 10	)	)
    assert out.count('hello'	) == 10
    assert out.count('there'	) == 10
    assert len(_SCREAMING_SNAKE_CASE	) == 20

  # check multiprocess from pathos (uses dill for pickling)
  with multiprocess.Pool(2	) as pool:
    lowercase__			    =						list(iflatmap_unordered(_SCREAMING_SNAKE_CASE	,		_split_text	,		kwargs_iterable=[{'text': 'hello there'}] * 10	)	)
    assert out.count('hello'	) == 10
    assert out.count('there'	) == 10
    assert len(_SCREAMING_SNAKE_CASE	) == 20

  # check that we get items as fast as possible
  with Pool(2	) as pool:
    lowercase__			    =						[]
    for yield_time, content in iflatmap_unordered(
        _SCREAMING_SNAKE_CASE	,		_aseconds_generator_of_aitems_with_timing	,		kwargs_iterable=[{'content': 'a'}, {'content': 'b'}]	):
      assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
      out.append(_SCREAMING_SNAKE_CASE	)
    assert out.count('a'	) == 2
    assert out.count('b'	) == 2
    assert len(_SCREAMING_SNAKE_CASE	) == 4









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()
lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowercase_					     =	{
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
  lowercase__			    =						EfficientNetConfig()
  lowercase__			    =						CONFIG_MAP[model_name]['hidden_dim']
  lowercase__			    =						CONFIG_MAP[model_name]['width_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['depth_coef']
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						CONFIG_MAP[model_name]['dropout_rate']
  lowercase__			    =						CONFIG_MAP[model_name]['dw_padding']

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

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




def     __UpperCamelCase						()       ->     Tuple:
  lowercase__			    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__			    =						Image.open(requests.get(_SCREAMING_SNAKE_CASE	,		stream=_SCREAMING_SNAKE_CASE	).raw	)
  return im




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__			    =						CONFIG_MAP[model_name]['image_size']
  lowercase__			    =						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=_SCREAMING_SNAKE_CASE	,		)
  return preprocessor




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
  lowercase__			    =						sorted(set(_SCREAMING_SNAKE_CASE	)	)
  lowercase__			    =						len(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						{b: str(_SCREAMING_SNAKE_CASE	) for b, i in zip(_SCREAMING_SNAKE_CASE	,		range(_SCREAMING_SNAKE_CASE	)	)}

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

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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

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

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




@torch.no_grad()
def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Tuple:
  lowercase__			    =						model_classes[model_name](
      include_top=_SCREAMING_SNAKE_CASE	,		weights='imagenet'	,		input_tensor=_SCREAMING_SNAKE_CASE	,		input_shape=_SCREAMING_SNAKE_CASE	,		pooling=_SCREAMING_SNAKE_CASE	,		classes=1000	,		classifier_activation='softmax'	,		)

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

  # Load HuggingFace model
  lowercase__			    =						get_efficientnet_config(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE	).eval()
  lowercase__			    =						hf_model.state_dict()

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

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

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

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

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

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


if __name__ == "__main__":
      lowercase_					     =	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""")

      lowercase_					     =	parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


