"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__     						=  {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__     						=  [
        """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TrOCRForCausalLM""",
        """TrOCRPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
    from .processing_trocr import TrOCRProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
    import sys

    UpperCAmelCase__     						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""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)
