

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase_             =     'src/diffusers'
lowercase_             =     '.'


# This is to make sure the diffusers module imported is the one in the repo.
lowercase_             =     importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowercase_             =     spec.loader.load_module()
def   a   (    A__			:     str   ,     A__			:     List[Any]  )					->   Tuple:





      """simple docstring"""



      return line.startswith(A__  ) or len(A__  ) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$'   ,     A__  ) is not None
def   a   (    A__			:     Tuple  )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =object_name.split('.'  )
      _lowercase			 =0

      # First let's find the module where our object lives.
      _lowercase			 =parts[i]
      while i < len(A__  ) and not os.path.isfile(os.path.join(A__   ,     F'''{module}.py'''  )  ):
            i += 1
            if i < len(A__  ):
                  _lowercase			 =os.path.join(A__   ,     parts[i]  )
      if i >= len(A__  ):
            raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''  )

      with open(os.path.join(A__   ,     F'''{module}.py'''  )   ,     'r'   ,     encoding='utf-8'   ,     newline='\n'  ) as f:
            _lowercase			 =f.readlines()

      # Now let's find the class / func in the code!
      _lowercase			 =''
      _lowercase			 =0
      for name in parts[i + 1 :]:
            while (
                line_index < len(A__  ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)'''   ,     lines[line_index]  ) is None
            ):
                  line_index += 1
            indent += "    "
            line_index += 1

      if line_index >= len(A__  ):
            raise ValueError(F''' {object_name} does not match any function or class in {module}.'''  )

      # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
      _lowercase			 =line_index
      while line_index < len(A__  ) and _should_continue(lines[line_index]   ,     A__  ):
            line_index += 1
      # Clean up empty lines at the end (if any).
      while len(lines[line_index - 1]  ) <= 1:
            line_index -= 1

      _lowercase			 =lines[start_index:line_index]
      return "".join(A__  )


lowercase_             =     re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
lowercase_             =     re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
lowercase_             =     re.compile(R'<FILL\s+[^>]*>')
def   a   (    A__			:     Tuple  )					->   Any:





      """simple docstring"""



      _lowercase			 =code.split('\n'  )
      _lowercase			 =0
      while idx < len(A__  ) and len(lines[idx]  ) == 0:
            idx += 1
      if idx < len(A__  ):
            return re.search(r'^(\s*)\S'   ,     lines[idx]  ).groups()[0]
      return ""
def   a   (    A__			:     Dict  )					->   int:





      """simple docstring"""



      _lowercase			 =len(get_indent(A__  )  ) > 0
      if has_indent:
            _lowercase			 =F'''class Bla:\n{code}'''
      _lowercase			 =black.Mode(target_versions={black.TargetVersion.PYaa}   ,     line_length=119   ,     preview=A__  )
      _lowercase			 =black.format_str(A__   ,     mode=A__  )
      _lowercase , _lowercase			 =style_docstrings_in_code(A__  )
      return result[len('class Bla:\n'  ) :] if has_indent else result
def   a   (    A__			:     List[str]   ,     A__			:     List[Any]=False  )					->   Tuple:





      """simple docstring"""



      with open(A__   ,     'r'   ,     encoding='utf-8'   ,     newline='\n'  ) as f:
            _lowercase			 =f.readlines()
      _lowercase			 =[]
      _lowercase			 =0
      # Not a for loop cause `lines` is going to change (if `overwrite=True`).
      while line_index < len(A__  ):
            _lowercase			 =_re_copy_warning.search(lines[line_index]  )
            if search is None:
                  line_index += 1
                  continue

            # There is some copied code here, let's retrieve the original.
            _lowercase , _lowercase , _lowercase			 =search.groups()
            _lowercase			 =find_code_in_diffusers(A__  )
            _lowercase			 =get_indent(A__  )

            _lowercase			 =line_index + 1 if indent == theoretical_indent else line_index + 2
            _lowercase			 =theoretical_indent
            _lowercase			 =start_index

            # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
            _lowercase			 =True
            while line_index < len(A__  ) and should_continue:
                  line_index += 1
                  if line_index >= len(A__  ):
                        break
                  _lowercase			 =lines[line_index]
                  _lowercase			 =_should_continue(A__   ,     A__  ) and re.search(F'''^{indent}# End copy'''   ,     A__  ) is None
            # Clean up empty lines at the end (if any).
            while len(lines[line_index - 1]  ) <= 1:
                  line_index -= 1

            _lowercase			 =lines[start_index:line_index]
            _lowercase			 =''.join(A__  )

            # Remove any nested `Copied from` comments to avoid circular copies
            _lowercase			 =[line for line in theoretical_code.split('\n'  ) if _re_copy_warning.search(A__  ) is None]
            _lowercase			 ='\n'.join(A__  )

            # Before comparing, use the `replace_pattern` on the original code.
            if len(A__  ) > 0:
                  _lowercase			 =replace_pattern.replace('with'   ,     ''  ).split(','  )
                  _lowercase			 =[_re_replace_pattern.search(A__  ) for p in patterns]
                  for pattern in patterns:
                        if pattern is None:
                              continue
                        _lowercase , _lowercase , _lowercase			 =pattern.groups()
                        _lowercase			 =re.sub(A__   ,     A__   ,     A__  )
                        if option.strip() == "all-casing":
                              _lowercase			 =re.sub(obja.lower()   ,     obja.lower()   ,     A__  )
                              _lowercase			 =re.sub(obja.upper()   ,     obja.upper()   ,     A__  )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
                  _lowercase			 =blackify(lines[start_index - 1] + theoretical_code  )
                  _lowercase			 =theoretical_code[len(lines[start_index - 1]  ) :]

            # Test for a diff and act accordingly.
            if observed_code != theoretical_code:
                  diffs.append([object_name, start_index]  )
                  if overwrite:
                        _lowercase			 =lines[:start_index] + [theoretical_code] + lines[line_index:]
                        _lowercase			 =start_index + 1

      if overwrite and len(A__  ) > 0:
            # Warn the user a file has been modified.
            print(F'''Detected changes, rewriting {filename}.'''  )
            with open(A__   ,     'w'   ,     encoding='utf-8'   ,     newline='\n'  ) as f:
                  f.writelines(A__  )
      return diffs
def   a   (    A__			:     bool = False  )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =glob.glob(os.path.join(A__   ,     '**/*.py'  )   ,     recursive=A__  )
      _lowercase			 =[]
      for filename in all_files:
            _lowercase			 =is_copy_consistent(A__   ,     A__  )
            diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
      if not overwrite and len(A__  ) > 0:
            _lowercase			 ='\n'.join(A__  )
            raise Exception(
                'Found the following copy inconsistencies:\n'
                + diff
                + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'  )


if __name__ == "__main__":
     lowercase_             =     argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     lowercase_             =     parser.parse_args()

     check_copies(args.fix_and_overwrite)





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      else:
            raise ValueError(F'''Task {task} not supported.'''  )

      print(F'''Building PyTorch model from configuration: {config}'''  )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(A__   ,     A__   ,     A__  )

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

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

      print('Used relative position embeddings:'   ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase_             =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )


