


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()
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     Tuple       =     {
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

SCREAMING_SNAKE_CASE__					:     Optional[int]       =     {
    '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    a__    (		snake_case__					:							Optional[Any]     ):
  _UpperCAmelCase		:							Optional[int]							=     EfficientNetConfig()
  _UpperCAmelCase		:							Tuple							=     CONFIG_MAP[model_name]["""hidden_dim"""]
  _UpperCAmelCase		:							Any							=     CONFIG_MAP[model_name]["""width_coef"""]
  _UpperCAmelCase		:							Optional[Any]							=     CONFIG_MAP[model_name]["""depth_coef"""]
  _UpperCAmelCase		:							int							=     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		:							int							=     """huggingface/label-files"""
  _UpperCAmelCase		:							Dict							=     """imagenet-1k-id2label.json"""
  _UpperCAmelCase		:							Optional[Any]							=     1000
  _UpperCAmelCase		:							List[str]							=     json.load(open(hf_hub_download(snake_case__						,   snake_case__						,   repo_type="""dataset"""     )						,   """r"""     )     )
  _UpperCAmelCase		:							Union[str, Any]							=     {int(snake_case__     ): v for k, v in idalabel.items()}

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




def    a__    (		):
  _UpperCAmelCase		:							Optional[int]							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase		:							str							=     Image.open(requests.get(snake_case__						,   stream=snake_case__     ).raw     )
  return im




def    a__    (		snake_case__					:							Tuple     ):
  _UpperCAmelCase		:							List[str]							=     CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase		:							str							=     EfficientNetImageProcessor(
      size={"""height""": size, """width""": size}						,   image_mean=[0.485, 0.456, 0.406]						,   image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]						,   do_center_crop=snake_case__						,   )
  return preprocessor




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

  _UpperCAmelCase		:							Union[str, Any]							=     []
  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		:							List[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		:							Tuple							=     """efficientnet.""" + item[1]

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




def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							int						,   snake_case__					:							List[str]     ):
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

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




@torch.no_grad()
def    a__    (		snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							int						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							List[str]							=     model_classes[model_name](
      include_top=snake_case__						,   weights="""imagenet"""						,   input_tensor=snake_case__						,   input_shape=snake_case__						,   pooling=snake_case__						,   classes=1000						,   classifier_activation="""softmax"""						,   )

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

  # Load HuggingFace model
  _UpperCAmelCase		:							Any							=     get_efficientnet_config(snake_case__     )
  _UpperCAmelCase		:							List[str]							=     EfficientNetForImageClassification(snake_case__     ).eval()
  _UpperCAmelCase		:							Any							=     hf_model.state_dict()

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

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

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

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     List[Any]       =     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')

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









import collections
import importlib.util
import os
import re
from pathlib import Path


SCREAMING_SNAKE_CASE__					:     List[Any]       =     'src/transformers'


# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__					:     List[Any]       =     re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
SCREAMING_SNAKE_CASE__					:     str       =     re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
SCREAMING_SNAKE_CASE__					:     Tuple       =     re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
SCREAMING_SNAKE_CASE__					:     Dict       =     re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma:     "MyModel",
SCREAMING_SNAKE_CASE__					:     Optional[int]       =     re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only:    ["foo", "bar"],
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
SCREAMING_SNAKE_CASE__					:     List[Any]       =     re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     re.compile(R'^\s*try:')
# Catches a line with else:
SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     re.compile(R'^\s*else:')




def    a__    (		snake_case__					:							Union[str, Any]     ):
  if _re_test_backend.search(snake_case__     ) is None:
    return None
  _UpperCAmelCase		:							str							=     [b[0] for b in _re_backend.findall(snake_case__     )]
  backends.sort()
  return "_and_".join(snake_case__     )




def    a__    (		snake_case__					:							Optional[int]     ):
  with open(snake_case__						,   """r"""						,   encoding="""utf-8"""						,   newline="""\n"""     ) as f:
    _UpperCAmelCase		:							Optional[Any]							=     f.readlines()

  _UpperCAmelCase		:							int							=     0
  while line_index < len(snake_case__     ) and not lines[line_index].startswith("""_import_structure = {"""     ):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(snake_case__     ):
    return None

  # First grab the objects without a specific backend in _import_structure
  _UpperCAmelCase		:							int							=     []
  while not lines[line_index].startswith("""if TYPE_CHECKING"""     ) and find_backend(lines[line_index]     ) is None:
    _UpperCAmelCase		:							int							=     lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(snake_case__     ):
      _UpperCAmelCase		:							Optional[Any]							=     _re_one_line_import_struct.search(snake_case__     ).groups()[0]
      _UpperCAmelCase		:							str							=     re.findall("""\[([^\]]+)\]"""						,   snake_case__     )
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(""", """     )]     )
      line_index += 1
      continue
    _UpperCAmelCase		:							int							=     _re_import_struct_key_value.search(snake_case__     )
    if single_line_import_search is not None:
      _UpperCAmelCase		:							Dict							=     [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """     ) if len(snake_case__     ) > 0]
      objects.extend(snake_case__     )
    elif line.startswith(""" """ * 8 + """\""""     ):
      objects.append(line[9:-3]     )
    line_index += 1

  _UpperCAmelCase		:							str							=     {"""none""": objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith("""if TYPE_CHECKING"""     ):
    # If the line is an if not is_backend_available, we grab all objects associated.
    _UpperCAmelCase		:							Tuple							=     find_backend(lines[line_index]     )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]     ) is None:
      _UpperCAmelCase		:							List[Any]							=     None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]     ) is None:
        line_index += 1

      line_index += 1

      _UpperCAmelCase		:							List[Any]							=     []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(""" """ * 4     ):
        _UpperCAmelCase		:							Optional[int]							=     lines[line_index]
        if _re_import_struct_add_one.search(snake_case__     ) is not None:
          objects.append(_re_import_struct_add_one.search(snake_case__     ).groups()[0]     )
        elif _re_import_struct_add_many.search(snake_case__     ) is not None:
          _UpperCAmelCase		:							str							=     _re_import_struct_add_many.search(snake_case__     ).groups()[0].split(""", """     )
          _UpperCAmelCase		:							Optional[Any]							=     [obj[1:-1] for obj in imports if len(snake_case__     ) > 0]
          objects.extend(snake_case__     )
        elif _re_between_brackets.search(snake_case__     ) is not None:
          _UpperCAmelCase		:							str							=     _re_between_brackets.search(snake_case__     ).groups()[0].split(""", """     )
          _UpperCAmelCase		:							List[str]							=     [obj[1:-1] for obj in imports if len(snake_case__     ) > 0]
          objects.extend(snake_case__     )
        elif _re_quote_object.search(snake_case__     ) is not None:
          objects.append(_re_quote_object.search(snake_case__     ).groups()[0]     )
        elif line.startswith(""" """ * 8 + """\""""     ):
          objects.append(line[9:-3]     )
        elif line.startswith(""" """ * 12 + """\""""     ):
          objects.append(line[13:-3]     )
        line_index += 1

      _UpperCAmelCase		:							str							=     objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  _UpperCAmelCase		:							Optional[Any]							=     []
  while (
      line_index < len(snake_case__     )
      and find_backend(lines[line_index]     ) is None
      and not lines[line_index].startswith("""else"""     )
  ):
    _UpperCAmelCase		:							Union[str, Any]							=     lines[line_index]
    _UpperCAmelCase		:							str							=     _re_import.search(snake_case__     )
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
    elif line.startswith(""" """ * 8     ):
      objects.append(line[8:-2]     )
    line_index += 1

  _UpperCAmelCase		:							int							=     {"""none""": objects}
  # Let's continue with backend-specific objects
  while line_index < len(snake_case__     ):
    # If the line is an if is_backend_available, we grab all objects associated.
    _UpperCAmelCase		:							str							=     find_backend(lines[line_index]     )
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]     ) is None:
      _UpperCAmelCase		:							Optional[int]							=     None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]     ) is None:
        line_index += 1

      line_index += 1

      _UpperCAmelCase		:							Dict							=     []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]     ) <= 1 or lines[line_index].startswith(""" """ * 8     ):
        _UpperCAmelCase		:							Union[str, Any]							=     lines[line_index]
        _UpperCAmelCase		:							Any							=     _re_import.search(snake_case__     )
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """     )     )
        elif line.startswith(""" """ * 12     ):
          objects.append(line[12:-2]     )
        line_index += 1

      _UpperCAmelCase		:							str							=     objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects




def    a__    (		snake_case__					:							Any						,   snake_case__					:							Optional[int]     ):

  def find_duplicates(snake_case__					:							Dict     ):
    return [k for k, v in collections.Counter(snake_case__     ).items() if v > 1]

  if list(import_dict_objects.keys()     ) != list(type_hint_objects.keys()     ):
    return ["Both sides of the init do not have the same backends!"]

  _UpperCAmelCase		:							int							=     []
  for key in import_dict_objects.keys():
    _UpperCAmelCase		:							Any							=     find_duplicates(import_dict_objects[key]     )
    if duplicate_imports:
      errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''     )
    _UpperCAmelCase		:							Optional[int]							=     find_duplicates(type_hint_objects[key]     )
    if duplicate_type_hints:
      errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''     )

    if sorted(set(import_dict_objects[key]     )     ) != sorted(set(type_hint_objects[key]     )     ):
      _UpperCAmelCase		:							Optional[int]							=     """base imports""" if key == """none""" else f'''{key} backend'''
      errors.append(f'''Differences for {name}:'''     )
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''     )
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''     )
  return errors




def    a__    (		):
  _UpperCAmelCase		:							Any							=     []
  for root, _, files in os.walk(snake_case__     ):
    if "__init__.py" in files:
      _UpperCAmelCase		:							Optional[Any]							=     os.path.join(snake_case__						,   """__init__.py"""     )
      _UpperCAmelCase		:							List[str]							=     parse_init(snake_case__     )
      if objects is not None:
        _UpperCAmelCase		:							int							=     analyze_results(*snake_case__     )
        if len(snake_case__     ) > 0:
          _UpperCAmelCase		:							Union[str, Any]							=     f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append("""\n""".join(snake_case__     )     )
  if len(snake_case__     ) > 0:
    raise ValueError("""\n\n""".join(snake_case__     )     )




def    a__    (		):
  _UpperCAmelCase		:							Tuple							=     []
  for path, directories, files in os.walk(snake_case__     ):
    for folder in directories:
      # Ignore private modules
      if folder.startswith("""_"""     ):
        directories.remove(snake_case__     )
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(snake_case__     ) / folder).glob("""*.py"""     )     )     ) == 0:
        continue
      _UpperCAmelCase		:							Dict							=     str((Path(snake_case__     ) / folder).relative_to(snake_case__     )     )
      _UpperCAmelCase		:							Union[str, Any]							=     short_path.replace(os.path.sep						,   """."""     )
      submodules.append(snake_case__     )
    for fname in files:
      if fname == "__init__.py":
        continue
      _UpperCAmelCase		:							Optional[Any]							=     str((Path(snake_case__     ) / fname).relative_to(snake_case__     )     )
      _UpperCAmelCase		:							Optional[Any]							=     short_path.replace(""".py"""						,   """"""     ).replace(os.path.sep						,   """."""     )
      if len(submodule.split("""."""     )     ) == 1:
        submodules.append(snake_case__     )
  return submodules


SCREAMING_SNAKE_CASE__					:     List[Any]       =     [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
]




def    a__    (		):
  # This is to make sure the transformers module imported is the one in the repo.
  _UpperCAmelCase		:							int							=     importlib.util.spec_from_file_location(
      """transformers"""						,   os.path.join(snake_case__						,   """__init__.py"""     )						,   submodule_search_locations=[PATH_TO_TRANSFORMERS]						,   )
  _UpperCAmelCase		:							Optional[int]							=     spec.loader.load_module()

  _UpperCAmelCase		:							int							=     [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
  ]
  if len(snake_case__     ) > 0:
    _UpperCAmelCase		:							Dict							=     """\n""".join(f'''- {module}''' for module in module_not_registered     )
    raise ValueError(
        """The following submodules are not properly registered in the main init of Transformers:\n"""
        f'''{list_of_modules}\n'''
        """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""     )


if __name__ == "__main__":
 check_all_inits()
 check_submodules()





