





"""simple docstring"""





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class 		lowercase					(					lowerCAmelCase_					):
      _SCREAMING_SNAKE_CASE					=      'gpt_neo'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}





      def __init__(       self      ,						lowercase=50_257      ,						lowercase=2_048      ,						lowercase=2_048      ,						lowercase=24      ,						lowercase=[[["global", "local"], 12]]      ,						lowercase=16      ,						lowercase=None      ,						lowercase=256      ,						lowercase="gelu_new"      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=0.0      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=50_256      ,						lowercase=50_256      ,						**lowercase      ,						)		->     List[Any]:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_layers
            lowerCAmelCase  						=       num_heads
            lowerCAmelCase  						=       intermediate_size
            lowerCAmelCase  						=       window_size
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_dropout
            lowerCAmelCase  						=       embed_dropout
            lowerCAmelCase  						=       attention_dropout
            lowerCAmelCase  						=       classifier_dropout
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       use_cache

            lowerCAmelCase  						=       bos_token_id
            lowerCAmelCase  						=       eos_token_id

            lowerCAmelCase  						=       attention_types
            lowerCAmelCase  						=       self.expand_attention_types_params(__lowerCAmelCase )

            if len(self.attention_layers ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f'but is `len(config.attention_layers) = {len(self.attention_layers )}`, '
                      f'`config.num_layers = {self.num_layers}`. '
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument.""" )

            super().__init__(bos_token_id=__lowerCAmelCase      ,						eos_token_id=__lowerCAmelCase      ,						**__lowerCAmelCase )





      @staticmethod
      def     _snake_case			(       lowercase )		->     List[str]:
            lowerCAmelCase  						=       []
            for item in attention_types:
                  for _ in range(item[1] ):
                        attentions.extend(item[0] )
            return attentions







def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      import torch

      lowerCAmelCase  						=       input.size()
      lowerCAmelCase  						=       len(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       shape[dimension]

      lowerCAmelCase  						=       torch.arange(0							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       torch.div(sizedim - size							,						SCREAMING_SNAKE_CASE							,						rounding_mode="""floor"""			) + 1
      lowerCAmelCase  						=       torch.arange(SCREAMING_SNAKE_CASE			) + low_indices[:min_length][:, None]

      lowerCAmelCase  						=       [slice(SCREAMING_SNAKE_CASE			)] * rank
      lowerCAmelCase  						=       indices
      lowerCAmelCase  						=       input[s]

      lowerCAmelCase  						=       list(range(0							,						rank + 1			)			)
      perm.append(perm.pop(dimension + 1			)			)

      return sliced.permute(SCREAMING_SNAKE_CASE			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      import torch

      lowerCAmelCase  						=       torch.arange(1							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       torch.remainder(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       remainders == 0
      lowerCAmelCase  						=       candidates[divisor_indices]
      lowerCAmelCase  						=       torch.max(SCREAMING_SNAKE_CASE			)
      return largest_divisor, torch.div(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						rounding_mode="""floor"""			)






class 		lowercase					(					lowerCAmelCase_					):





      @property
      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
            if self.use_past:
                  self.fill_with_past_key_values_(__lowerCAmelCase      ,						direction="""inputs""" )
                  lowerCAmelCase  						=       {0: """batch""", 1: """past_sequence + sequence"""}
            else:
                  lowerCAmelCase  						=       {0: """batch""", 1: """sequence"""}

            return common_inputs





      @property
      def     _snake_case			(       self )		->     Any:
            return self._config.num_heads





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = -1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						)		->     Any:
            lowerCAmelCase  						=       super(__lowerCAmelCase      ,						self ).generate_dummy_inputs(
                __lowerCAmelCase      ,						batch_size=__lowerCAmelCase      ,						seq_length=__lowerCAmelCase      ,						is_pair=__lowerCAmelCase      ,						framework=__lowerCAmelCase )

            # We need to order the input in the way they appears in the forward()
            lowerCAmelCase  						=       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )

            # Need to add the past_keys
            if self.use_past:
                  if not is_torch_available():
                        raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
                  else:
                        import torch

                        lowerCAmelCase						,		lowerCAmelCase  						=       common_inputs["""input_ids"""].shape
                        # Not using the same length for past_key_values
                        lowerCAmelCase  						=       seqlen + 2
                        lowerCAmelCase  						=       (
                            batch,
                            self.num_attention_heads,
                            past_key_values_length,
                            self._config.hidden_size // self.num_attention_heads,
                        )
                        lowerCAmelCase  						=       [
                            (torch.zeros(__lowerCAmelCase ), torch.zeros(__lowerCAmelCase )) for _ in range(self.num_layers )
                        ]

            lowerCAmelCase  						=       common_inputs["""attention_mask"""]
            if self.use_past:
                  lowerCAmelCase  						=       ordered_inputs["""attention_mask"""].dtype
                  lowerCAmelCase  						=       torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(__lowerCAmelCase      ,						__lowerCAmelCase      ,						dtype=__lowerCAmelCase )]      ,						dim=1 )

            return ordered_inputs





      @property
      def     _snake_case			(       self )		->     Optional[int]:
            return 13







"""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()
UpperCAmelCase__     						=  logging.get_logger(__name__)

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

UpperCAmelCase__     						=  {
    """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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					EfficientNetConfig()
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""hidden_dim"""]
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""width_coef"""]
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""depth_coef"""]
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""dropout_rate"""]
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""dw_padding"""]

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

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






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase         =					Image.open(requests.get(lowercase      ,stream=lowercase						).raw						)
  return im






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase         =					EfficientNetImageProcessor(
      size={"""height""": size, """width""": size}      ,image_mean=[0.4_85, 0.4_56, 0.4_06]      ,image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]      ,do_center_crop=lowercase      ,)
  return preprocessor






def 			__UpperCAmelCase    (	lowercase						):







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

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

  _UpperCAmelCase         =					"""classifier.weight"""
  _UpperCAmelCase         =					"""classifier.bias"""
  return key_mapping






def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase						):







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

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

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






@torch.no_grad()
def 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					model_classes[model_name](
      include_top=lowercase      ,weights="""imagenet"""      ,input_tensor=lowercase      ,input_shape=lowercase      ,pooling=lowercase      ,classes=10_00      ,classifier_activation="""softmax"""      ,)

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

  # Load HuggingFace model
  _UpperCAmelCase         =					get_efficientnet_config(lowercase						)
  _UpperCAmelCase         =					EfficientNetForImageClassification(lowercase						).eval()
  _UpperCAmelCase         =					hf_model.state_dict()

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

  # Initialize preprocessor and preprocess input image
  _UpperCAmelCase         =					convert_image_processor(lowercase						)
  _UpperCAmelCase         =					preprocessor(images=prepare_img()      ,return_tensors="""pt"""						)

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

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

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

  if push_to_hub:
    # Push model and image processor to hub
    print(f'''Pushing converted {model_name} to the hub...'''						)
    _UpperCAmelCase         =					f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(lowercase						)
    hf_model.push_to_hub(lowercase						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  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""")

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