


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List
def   snake_case_    (			__lowercase      ,							__lowercase							):
   if name != "training_function" and name != "main":
      raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
   UpperCAmelCase_		,   UpperCAmelCase_				:   List[Any]							= [], False
   for line in lines:
      if not found_start and F'''def {name}''' in line:
         UpperCAmelCase_				:   Any							= True
         good_lines.append(__lowercase							)
         continue
      if found_start:
         if name == "training_function" and "def main" in line:
            return good_lines
         if name == "main" and "if __name__" in line:
            return good_lines
         good_lines.append(__lowercase							)
def   snake_case_    (			__lowercase							):
   return [line for line in lines if not line.lstrip().startswith('''#'''							) and line != "\n"]


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None							):
   with open(__lowercase      ,							'''r'''							) as f:
      UpperCAmelCase_				:   Union[str, Any]							= f.readlines()
   with open(os.path.abspath(os.path.join('''examples'''      ,							'''nlp_example.py'''							)							)      ,							'''r'''							) as f:
      UpperCAmelCase_				:   Optional[Any]							= f.readlines()
   with open(__lowercase      ,							'''r'''							) as f:
      UpperCAmelCase_				:   str							= f.readlines()
   if secondary_filename is not None:
      with open(__lowercase      ,							'''r'''							) as f:
         UpperCAmelCase_				:   Optional[int]							= f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
   if parser_only:
      UpperCAmelCase_				:   str							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''main'''							)							)
      UpperCAmelCase_				:   Dict							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''main'''							)							)
      UpperCAmelCase_				:   Optional[Any]							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''main'''							)							)
      if secondary_filename is not None:
         UpperCAmelCase_				:   Optional[Any]							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''main'''							)							)
   else:
      UpperCAmelCase_				:   Tuple							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''training_function'''							)							)
      UpperCAmelCase_				:   Dict							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''training_function'''							)							)
      UpperCAmelCase_				:   List[Any]							= clean_lines(get_function_contents_by_name(__lowercase      ,							'''training_function'''							)							)
      if secondary_filename is not None:
         UpperCAmelCase_				:   Union[str, Any]							= clean_lines(
             get_function_contents_by_name(__lowercase      ,							'''training_function'''							)							)

   UpperCAmelCase_				:   Optional[Any]							= '''train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'''

   # Specific code in our script that differs from the full version, aka what is new
   UpperCAmelCase_				:   str							= []
   UpperCAmelCase_				:   str							= []  # We keep track of the idxs just in case it's a repeated statement
   UpperCAmelCase_				:   List[Any]							= iter(__lowercase							)
   for i in range(len(__lowercase							) - 1							):
      if i not in passed_idxs:
         UpperCAmelCase_				:   str							= next(__lowercase							)
         if (line not in full_file_func) and (line.lstrip() != _dl_line):
            if "TESTING_MOCKED_DATALOADERS" not in line:
               new_feature_code.append(__lowercase							)
               passed_idxs.append(__lowercase							)
            else:
               # Skip over the `config['num_epochs'] = 2` statement
               UpperCAmelCase_				:   Dict							= next(__lowercase							)

    # Extract out just the new parts from the full_file_training_func
   UpperCAmelCase_				:   List[str]							= []
   UpperCAmelCase_				:   Dict							= []  # We keep track of the idxs just in case it's a repeated statement
   for i, line in enumerate(__lowercase							):
      if i not in passed_idxs:
         if (line not in full_file_func) and (line.lstrip() != _dl_line):
            if "TESTING_MOCKED_DATALOADERS" not in line:
               new_full_example_parts.append(__lowercase							)
               passed_idxs.append(__lowercase							)

    # Finally, get the overall diff
   UpperCAmelCase_				:   Optional[int]							= [line for line in new_feature_code if line not in new_full_example_parts]
   if secondary_filename is not None:
      UpperCAmelCase_				:   str							= [line for line in full_file_contents if line not in secondary_file_func]
      UpperCAmelCase_				:   Union[str, Any]							= [line for line in diff_from_example if line not in diff_from_two]

   return diff_from_example



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def   snake_case_    (			__lowercase      ,							__lowercase							):
   # Initialize PyTorch model
   UpperCAmelCase_				:   Optional[Any]							= CanineConfig()
   UpperCAmelCase_				:   Any							= CanineModel(__lowercase							)
   model.eval()

   print(F'''Building PyTorch model from configuration: {config}'''							)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(__lowercase      ,							__lowercase      ,							__lowercase							)

   # Save pytorch-model (weights and configuration)
   print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
   model.save_pretrained(__lowercase							)

   # Save tokenizer files
   UpperCAmelCase_				:   List[Any]							= CanineTokenizer()
   print(F'''Save tokenizer files to {pytorch_dump_path}'''							)
   tokenizer.save_pretrained(__lowercase							)


if __name__ == "__main__":
       __UpperCamelCase					:	int          =				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path',
           default=None,
           type=str,
           required=True,
           help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
       )
       parser.add_argument(
           '--pytorch_dump_path',
           default=None,
           type=str,
           required=True,
           help='Path to a folder where the PyTorch model will be placed.',
       )
       __UpperCamelCase					:	str          =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)