

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
_a		       =   '''src/diffusers'''
_a		       =   '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
_a		       =   importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_a		       =   spec.loader.load_module()







def _a (       SCREAMING_SNAKE_CASE		:			Optional[int]    ,					SCREAMING_SNAKE_CASE		:			Optional[int]		)    ->    Optional[Any]:

 """simple docstring"""






 return line.startswith(SCREAMING_SNAKE_CASE		) or len(SCREAMING_SNAKE_CASE		) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$'    ,					SCREAMING_SNAKE_CASE		) is not None







def _a (       SCREAMING_SNAKE_CASE		:			Dict		)    ->    Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:   Tuple   	=      object_name.split('.'		)
 __lowerCAmelCase:   Tuple   	=      0

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

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

 # Now let's find the class / func in the code!
 __lowerCAmelCase:   int   	=      ''
 __lowerCAmelCase:   Tuple   	=      0
 for name in parts[i + 1 :]:
  while (
      line_index < len(SCREAMING_SNAKE_CASE		) 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(SCREAMING_SNAKE_CASE		):
  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).
 __lowerCAmelCase:   Any   	=      line_index
 while line_index < len(SCREAMING_SNAKE_CASE		) and _should_continue(lines[line_index]    ,					SCREAMING_SNAKE_CASE		):
  line_index += 1
 # Clean up empty lines at the end (if any).
 while len(lines[line_index - 1]		) <= 1:
  line_index -= 1

 __lowerCAmelCase:   Optional[int]   	=      lines[start_index:line_index]
 return "".join(SCREAMING_SNAKE_CASE		)


_a		       =   re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_a		       =   re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_a		       =   re.compile(R'''<FILL\s+[^>]*>''')







def _a (       SCREAMING_SNAKE_CASE		:			Tuple		)    ->    List[str]:

 """simple docstring"""






 __lowerCAmelCase:   int   	=      code.split('\n'		)
 __lowerCAmelCase:   Tuple   	=      0
 while idx < len(SCREAMING_SNAKE_CASE		) and len(lines[idx]		) == 0:
  idx += 1
 if idx < len(SCREAMING_SNAKE_CASE		):
  return re.search(R'^(\s*)\S'    ,					lines[idx]		).groups()[0]
 return ""







def _a (       SCREAMING_SNAKE_CASE		:			Dict		)    ->    Tuple:

 """simple docstring"""






 __lowerCAmelCase:   List[str]   	=      len(get_indent(SCREAMING_SNAKE_CASE		)		) > 0
 if has_indent:
  __lowerCAmelCase:   List[Any]   	=      f'''class Bla:\n{code}'''
 __lowerCAmelCase:   Union[str, Any]   	=      black.Mode(target_versions={black.TargetVersion.PYaa}    ,					line_length=1_19    ,					preview=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   Dict   	=      black.format_str(SCREAMING_SNAKE_CASE    ,					mode=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase	,							__lowerCAmelCase:   Any   	=      style_docstrings_in_code(SCREAMING_SNAKE_CASE		)
 return result[len('class Bla:\n'		) :] if has_indent else result







def _a (       SCREAMING_SNAKE_CASE		:			Union[str, Any]    ,					SCREAMING_SNAKE_CASE		:			int=False		)    ->    Tuple:

 """simple docstring"""






 with open(SCREAMING_SNAKE_CASE    ,					'r'    ,					encoding='utf-8'    ,					newline='\n'		) as f:
  __lowerCAmelCase:   str   	=      f.readlines()
 __lowerCAmelCase:   Any   	=      []
 __lowerCAmelCase:   Optional[Any]   	=      0
 # Not a for loop cause `lines` is going to change (if `overwrite=True`).
 while line_index < len(SCREAMING_SNAKE_CASE		):
  __lowerCAmelCase:   Any   	=      _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.
  __lowerCAmelCase	,							__lowerCAmelCase	,							__lowerCAmelCase:   str   	=      search.groups()
  __lowerCAmelCase:   Optional[Any]   	=      find_code_in_diffusers(SCREAMING_SNAKE_CASE		)
  __lowerCAmelCase:   Tuple   	=      get_indent(SCREAMING_SNAKE_CASE		)

  __lowerCAmelCase:   Any   	=      line_index + 1 if indent == theoretical_indent else line_index + 2
  __lowerCAmelCase:   Optional[int]   	=      theoretical_indent
  __lowerCAmelCase:   int   	=      start_index

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

  __lowerCAmelCase:   int   	=      lines[start_index:line_index]
  __lowerCAmelCase:   Optional[int]   	=      ''.join(SCREAMING_SNAKE_CASE		)

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

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

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

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







def _a (       SCREAMING_SNAKE_CASE		:			List[Any] = False		)    ->    List[Any]:

 """simple docstring"""






 __lowerCAmelCase:   Tuple   	=      glob.glob(os.path.join(SCREAMING_SNAKE_CASE    ,					'**/*.py'		)    ,					recursive=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:   List[str]   	=      []
 for filename in all_files:
  __lowerCAmelCase:   Union[str, Any]   	=      is_copy_consistent(SCREAMING_SNAKE_CASE    ,					SCREAMING_SNAKE_CASE		)
  diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
 if not overwrite and len(SCREAMING_SNAKE_CASE		) > 0:
  __lowerCAmelCase:   Optional[Any]   	=      '\n'.join(SCREAMING_SNAKE_CASE		)
  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__":
  _a		       =   argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  _a		       =   parser.parse_args()

  check_copies(args.fix_and_overwrite)






"""simple docstring"""


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 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """simple docstring"""
  # Initialise PyTorch model.
  # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
  # TapasConfig to False.

  # initialize configuration from json file
  _UpperCAmelCase         =					TapasConfig.from_json_file(lowercase						)
  # set absolute/relative position embeddings parameter
  _UpperCAmelCase         =					reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WTQ":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					True
    # hparam_utils.py hparams
    _UpperCAmelCase         =					0.66_46_94
    _UpperCAmelCase         =					0.20_79_51
    _UpperCAmelCase         =					0.12_11_94
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					False
    _UpperCAmelCase         =					0.0_35_25_13

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					False
    # hparam_utils.py hparams
    _UpperCAmelCase         =					36.45_19
    _UpperCAmelCase         =					0.90_34_21
    _UpperCAmelCase         =					2_22.0_88
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					0.76_31_41

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "TABFACT":
    _UpperCAmelCase         =					TapasForSequenceClassification(config=lowercase						)
  elif task == "MLM":
    _UpperCAmelCase         =					TapasForMaskedLM(config=lowercase						)
  elif task == "INTERMEDIATE_PRETRAINING":
    _UpperCAmelCase         =					TapasModel(config=lowercase						)
  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(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
  print(f'''Save tokenizer files to {pytorch_dump_path}'''						)
  _UpperCAmelCase         =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""      ,model_max_length=5_12						)
  tokenizer.save_pretrained(lowercase						)

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


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