



'''simple docstring'''






import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
A_							: List[str]						 =	"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
A_							: Optional[Any]						 =	direct_transformers_import(PATH_TO_TRANSFORMERS)

A_							: Union[str, Any]						 =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

A_							: Dict						 =	{
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)




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





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[Any]			     = False
       for attribute in attributes:
              for modeling_source in source_strings:
                     # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
                     if (
                         F'''config.{attribute}''' in modeling_source
                         or F'''getattr(config, "{attribute}"''' in modeling_source
                         or F'''getattr(self.config, "{attribute}"''' in modeling_source
                     ):
                            _UpperCAmelCase  :						Tuple			     = True
                     # Deal with multi-line cases
                     elif (
                         re.search(
                             RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''       ,   lowerCAmelCase_       ,   )
                         is not None
                     ):
                            _UpperCAmelCase  :						Any			     = True
                     # `SequenceSummary` is called with `SequenceSummary(config)`
                     elif attribute in [
                         "summary_type",
                         "summary_use_proj",
                         "summary_activation",
                         "summary_last_dropout",
                         "summary_proj_to_labels",
                         "summary_first_dropout",
                     ]:
                            if "SequenceSummary" in modeling_source:
                                   _UpperCAmelCase  :						List[str]			     = True
                     if attribute_used:
                            break
              if attribute_used:
                     break

    # common and important attributes, even if they do not always appear in the modeling files
       _UpperCAmelCase  :						Dict			     = [
           """bos_index""",
           """eos_index""",
           """pad_index""",
           """unk_index""",
           """mask_index""",
           """image_size""",
           """use_cache""",
           """out_features""",
           """out_indices""",
       ]
       _UpperCAmelCase  :						int			     = ["""encoder_no_repeat_ngram_size"""]

       # Special cases to be allowed
       _UpperCAmelCase  :						Optional[Any]			     = True
       if not attribute_used:
              _UpperCAmelCase  :						List[Any]			     = False
              for attribute in attributes:
                     # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
                     if attribute in ["is_encoder_decoder"] and default_value is True:
                            _UpperCAmelCase  :						Tuple			     = True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            _UpperCAmelCase  :						Any			     = True

                     # Allow cases without checking the default value in the configuration class
                     elif attribute in attributes_to_allow + attributes_used_in_generation:
                            _UpperCAmelCase  :						Dict			     = True
                     elif attribute.endswith("""_token_id"""		):
                            _UpperCAmelCase  :						Optional[int]			     = True

                     # configuration class specific cases
                     if not case_allowed:
                            _UpperCAmelCase  :						int			     = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__       ,   []		)
                            _UpperCAmelCase  :						Union[str, Any]			     = allowed_cases is True or attribute in allowed_cases

       return attribute_used or case_allowed




def     snake_case_					(     lowerCAmelCase_		)->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Optional[int]			     = dict(inspect.signature(config_class.__init__		).parameters		)
       _UpperCAmelCase  :						Optional[int]			     = [x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
       _UpperCAmelCase  :						Optional[int]			     = [signature[param].default for param in parameter_names]

       # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
       # as one variant is used, the test should pass
       _UpperCAmelCase  :						List[Any]			     = {}
       if len(config_class.attribute_map		) > 0:
              _UpperCAmelCase  :						Optional[int]			     = {v: k for k, v in config_class.attribute_map.items()}

       # Get the path to modeling source files
       _UpperCAmelCase  :						int			     = inspect.getsourcefile(lowerCAmelCase_		)
       _UpperCAmelCase  :						str			     = os.path.dirname(lowerCAmelCase_		)
       # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
       _UpperCAmelCase  :						Optional[int]			     = [os.path.join(lowerCAmelCase_       ,   lowerCAmelCase_		) for fn in os.listdir(lowerCAmelCase_		) if fn.startswith("""modeling_"""		)]

       # Get the source code strings
       _UpperCAmelCase  :						str			     = []
       for path in modeling_paths:
              if os.path.isfile(lowerCAmelCase_		):
                     with open(lowerCAmelCase_		) as fp:
                            modeling_sources.append(fp.read()		)

       _UpperCAmelCase  :						Any			     = []
       for config_param, default_value in zip(lowerCAmelCase_       ,   lowerCAmelCase_		):
              # `attributes` here is all the variant names for `config_param`
              _UpperCAmelCase  :						List[str]			     = [config_param]
              # some configuration classes have non-empty `attribute_map`, and both names could be used in the
              # corresponding modeling files. As long as one of them appears, it is fine.
              if config_param in reversed_attribute_map:
                     attributes.append(reversed_attribute_map[config_param]		)

              if not check_attribute_being_used(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		):
                     unused_attributes.append(attributes[0]		)

       return sorted(lowerCAmelCase_		)




def     snake_case_					(     )->		Optional[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Dict			     = {}
       for _config_class in list(CONFIG_MAPPING.values()		):
              # Skip deprecated models
              if "models.deprecated" in _config_class.__module__:
                     continue
              # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
              _UpperCAmelCase  :						List[Any]			     = [
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class		)       ,   lambda lowerCAmelCase_		: inspect.isclass(lowerCAmelCase_		)
                      and issubclass(lowerCAmelCase_       ,   lowerCAmelCase_		)
                      and inspect.getmodule(lowerCAmelCase_		) == inspect.getmodule(_config_class		)       ,   )
              ]
              for config_class in config_classes_in_module:
                     _UpperCAmelCase  :						Optional[int]			     = check_config_attributes_being_used(lowerCAmelCase_		)
                     if len(lowerCAmelCase_		) > 0:
                            _UpperCAmelCase  :						Tuple			     = unused_attributes

       if len(lowerCAmelCase_		) > 0:
              _UpperCAmelCase  :						Dict			     = """The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
              for name, attributes in configs_with_unused_attributes.items():
                     error += F'''{name}: {attributes}\n'''

              raise ValueError(lowerCAmelCase_		)


if __name__ == "__main__":
   check_config_attributes()








'''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)



